A “Reverse-Schur” Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design

PubMed Central

Bardhan, Jaydeep P.; Altman, Michael D.

2009-01-01

We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule’s electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts–in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method. PMID:23055839

Optimization of Stability Constrained Geometrically Nonlinear Shallow Trusses Using an Arc Length Sparse Method with a Strain Energy Density Approach

NASA Technical Reports Server (NTRS)

Hrinda, Glenn A.; Nguyen, Duc T.

2008-01-01

A technique for the optimization of stability constrained geometrically nonlinear shallow trusses with snap through behavior is demonstrated using the arc length method and a strain energy density approach within a discrete finite element formulation. The optimization method uses an iterative scheme that evaluates the design variables' performance and then updates them according to a recursive formula controlled by the arc length method. A minimum weight design is achieved when a uniform nonlinear strain energy density is found in all members. This minimal condition places the design load just below the critical limit load causing snap through of the structure. The optimization scheme is programmed into a nonlinear finite element algorithm to find the large strain energy at critical limit loads. Examples of highly nonlinear trusses found in literature are presented to verify the method.

High Order Entropy-Constrained Residual VQ for Lossless Compression of Images

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Smith, Mark J. T.; Scales, Allen

1995-01-01

High order entropy coding is a powerful technique for exploiting high order statistical dependencies. However, the exponentially high complexity associated with such a method often discourages its use. In this paper, an entropy-constrained residual vector quantization method is proposed for lossless compression of images. The method consists of first quantizing the input image using a high order entropy-constrained residual vector quantizer and then coding the residual image using a first order entropy coder. The distortion measure used in the entropy-constrained optimization is essentially the first order entropy of the residual image. Experimental results show very competitive performance.

Management of Occupational Exposure to Engineered Nanoparticles Through a Chance-Constrained Nonlinear Programming Approach

PubMed Central

Chen, Zhi; Yuan, Yuan; Zhang, Shu-Shen; Chen, Yu; Yang, Feng-Lin

2013-01-01

Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and manufactured nanomaterials (MNMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a chance-constrained nonlinear programming (CCNLP) optimization approach, which is developed to maximize the nanaomaterial production and minimize the risks of workplace exposure to MNMs. The CCNLP method integrates nonlinear programming (NLP) and chance-constrained programming (CCP), and handles uncertainties associated with both the nanomaterial production and workplace exposure control. The CCNLP method was examined through a single-walled carbon nanotube (SWNT) manufacturing process. The study results provide optimal production strategies and alternatives. It reveal that a high control measure guarantees that environmental health and safety (EHS) standards regulations are met, while a lower control level leads to increased risk of violating EHS regulations. The CCNLP optimization approach is a decision support tool for the optimization of the increasing MNMS manufacturing with workplace safety constraints under uncertainties. PMID:23531490

Management of occupational exposure to engineered nanoparticles through a chance-constrained nonlinear programming approach.

PubMed

Chen, Zhi; Yuan, Yuan; Zhang, Shu-Shen; Chen, Yu; Yang, Feng-Lin

2013-03-26

Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and manufactured nanomaterials (MNMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a chance-constrained nonlinear programming (CCNLP) optimization approach, which is developed to maximize the nanaomaterial production and minimize the risks of workplace exposure to MNMs. The CCNLP method integrates nonlinear programming (NLP) and chance-constrained programming (CCP), and handles uncertainties associated with both the nanomaterial production and workplace exposure control. The CCNLP method was examined through a single-walled carbon nanotube (SWNT) manufacturing process. The study results provide optimal production strategies and alternatives. It reveal that a high control measure guarantees that environmental health and safety (EHS) standards regulations are met, while a lower control level leads to increased risk of violating EHS regulations. The CCNLP optimization approach is a decision support tool for the optimization of the increasing MNMS manufacturing with workplace safety constraints under uncertainties.

An indirect method for numerical optimization using the Kreisselmeir-Steinhauser function

NASA Technical Reports Server (NTRS)

Wrenn, Gregory A.

1989-01-01

A technique is described for converting a constrained optimization problem into an unconstrained problem. The technique transforms one of more objective functions into reduced objective functions, which are analogous to goal constraints used in the goal programming method. These reduced objective functions are appended to the set of constraints and an envelope of the entire function set is computed using the Kreisselmeir-Steinhauser function. This envelope function is then searched for an unconstrained minimum. The technique may be categorized as a SUMT algorithm. Advantages of this approach are the use of unconstrained optimization methods to find a constrained minimum without the draw down factor typical of penalty function methods, and that the technique may be started from the feasible or infeasible design space. In multiobjective applications, the approach has the advantage of locating a compromise minimum design without the need to optimize for each individual objective function separately.

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

NASA Technical Reports Server (NTRS)

Heath, Christopher M.; Gray, Justin S.

2012-01-01

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Fast alternating projection methods for constrained tomographic reconstruction

PubMed Central

Liu, Li; Han, Yongxin

2017-01-01

The alternating projection algorithms are easy to implement and effective for large-scale complex optimization problems, such as constrained reconstruction of X-ray computed tomography (CT). A typical method is to use projection onto convex sets (POCS) for data fidelity, nonnegative constraints combined with total variation (TV) minimization (so called TV-POCS) for sparse-view CT reconstruction. However, this type of method relies on empirically selected parameters for satisfactory reconstruction and is generally slow and lack of convergence analysis. In this work, we use a convex feasibility set approach to address the problems associated with TV-POCS and propose a framework using full sequential alternating projections or POCS (FS-POCS) to find the solution in the intersection of convex constraints of bounded TV function, bounded data fidelity error and non-negativity. The rationale behind FS-POCS is that the mathematically optimal solution of the constrained objective function may not be the physically optimal solution. The breakdown of constrained reconstruction into an intersection of several feasible sets can lead to faster convergence and better quantification of reconstruction parameters in a physical meaningful way than that in an empirical way of trial-and-error. In addition, for large-scale optimization problems, first order methods are usually used. Not only is the condition for convergence of gradient-based methods derived, but also a primal-dual hybrid gradient (PDHG) method is used for fast convergence of bounded TV. The newly proposed FS-POCS is evaluated and compared with TV-POCS and another convex feasibility projection method (CPTV) using both digital phantom and pseudo-real CT data to show its superior performance on reconstruction speed, image quality and quantification. PMID:28253298

A TV-constrained decomposition method for spectral CT

NASA Astrophysics Data System (ADS)

Guo, Xiaoyue; Zhang, Li; Xing, Yuxiang

2017-03-01

Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that decomposition step in spectral CT imaging causes noise amplification and artifacts in component coefficient images. In this work, we propose materials decomposition via an optimization method to improve the quality of decomposed coefficient images. On the basis of general optimization problem, total variance minimization is constrained on coefficient images in our overall objective function with adjustable weights. We solve this constrained optimization problem under the framework of ADMM. Validation on both a numerical dental phantom in simulation and a real phantom of pig leg on a practical CT system using dual-energy imaging is executed. Both numerical and physical experiments give visually obvious better reconstructions than a general direct inverse method. SNR and SSIM are adopted to quantitatively evaluate the image quality of decomposed component coefficients. All results demonstrate that the TV-constrained decomposition method performs well in reducing noise without losing spatial resolution so that improving the image quality. The method can be easily incorporated into different types of spectral imaging modalities, as well as for cases with energy channels more than two.

WE-FG-207B-05: Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niu, S; Zhang, Y; Ma, J

Purpose: To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high-quality images to guide the reconstruction of energy-specific images. In PICTGV method, the high-quality image is reconstructed from all detector-collected X-ray signals and is referred as the broad-spectrum image. Distinctmore » from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back-projection method as well as TGV based method without prior image. Results: On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%. Conclusion: The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.« less

Application’s Method of Quadratic Programming for Optimization of Portfolio Selection

NASA Astrophysics Data System (ADS)

Kawamoto, Shigeru; Takamoto, Masanori; Kobayashi, Yasuhiro

Investors or fund-managers face with optimization of portfolio selection, which means that determine the kind and the quantity of investment among several brands. We have developed a method to obtain optimal stock’s portfolio more rapidly from twice to three times than conventional method with efficient universal optimization. The method is characterized by quadratic matrix of utility function and constrained matrices divided into several sub-matrices by focusing on structure of these matrices.

A hierarchical transition state search algorithm

NASA Astrophysics Data System (ADS)

del Campo, Jorge M.; Köster, Andreas M.

2008-07-01

A hierarchical transition state search algorithm is developed and its implementation in the density functional theory program deMon2k is described. This search algorithm combines the double ended saddle interpolation method with local uphill trust region optimization. A new formalism for the incorporation of the distance constrain in the saddle interpolation method is derived. The similarities between the constrained optimizations in the local trust region method and the saddle interpolation are highlighted. The saddle interpolation and local uphill trust region optimizations are validated on a test set of 28 representative reactions. The hierarchical transition state search algorithm is applied to an intramolecular Diels-Alder reaction with several internal rotors, which makes automatic transition state search rather challenging. The obtained reaction mechanism is discussed in the context of the experimentally observed product distribution.

Constrained optimization of sequentially generated entangled multiqubit states

NASA Astrophysics Data System (ADS)

Saberi, Hamed; Weichselbaum, Andreas; Lamata, Lucas; Pérez-García, David; von Delft, Jan; Solano, Enrique

2009-08-01

We demonstrate how the matrix-product state formalism provides a flexible structure to solve the constrained optimization problem associated with the sequential generation of entangled multiqubit states under experimental restrictions. We consider a realistic scenario in which an ancillary system with a limited number of levels performs restricted sequential interactions with qubits in a row. The proposed method relies on a suitable local optimization procedure, yielding an efficient recipe for the realistic and approximate sequential generation of any entangled multiqubit state. We give paradigmatic examples that may be of interest for theoretical and experimental developments.

Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Junsheng; Peng, Yanfeng; Yang, Yu; Wu, Zhantao

2017-02-01

Enlightened by ASTFA method, adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. In ASNBD method, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate an intrinsic narrow-band component (INBC). ASNBD is proposed aiming at solving the problems existed in ASTFA. Gauss-Newton type method, which is applied to solve the optimization problem in ASTFA, is irreplaceable and very sensitive to initial values. However, more appropriate optimization method such as genetic algorithm (GA) can be utilized to solve the optimization problem in ASNBD. Meanwhile, compared with ASTFA, the decomposition results generated by ASNBD have better physical meaning by constraining the components to be local narrow-band signals. Comparisons are made between ASNBD, ASTFA and EMD by analyzing simulation and experimental signals. The results indicate that ASNBD method is superior to the other two methods in generating more accurate components from noise signal, restraining the boundary effect, possessing better orthogonality and diagnosing rolling element bearing fault.

Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

PubMed

Crown, William; Buyukkaramikli, Nasuh; Thokala, Praveen; Morton, Alec; Sir, Mustafa Y; Marshall, Deborah A; Tosh, Jon; Padula, William V; Ijzerman, Maarten J; Wong, Peter K; Pasupathy, Kalyan S

2017-03-01

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Stability-Constrained Aerodynamic Shape Optimization with Applications to Flying Wings

NASA Astrophysics Data System (ADS)

Mader, Charles Alexander

A set of techniques is developed that allows the incorporation of flight dynamics metrics as an additional discipline in a high-fidelity aerodynamic optimization. Specifically, techniques for including static stability constraints and handling qualities constraints in a high-fidelity aerodynamic optimization are demonstrated. These constraints are developed from stability derivative information calculated using high-fidelity computational fluid dynamics (CFD). Two techniques are explored for computing the stability derivatives from CFD. One technique uses an automatic differentiation adjoint technique (ADjoint) to efficiently and accurately compute a full set of static and dynamic stability derivatives from a single steady solution. The other technique uses a linear regression method to compute the stability derivatives from a quasi-unsteady time-spectral CFD solution, allowing for the computation of static, dynamic and transient stability derivatives. Based on the characteristics of the two methods, the time-spectral technique is selected for further development, incorporated into an optimization framework, and used to conduct stability-constrained aerodynamic optimization. This stability-constrained optimization framework is then used to conduct an optimization study of a flying wing configuration. This study shows that stability constraints have a significant impact on the optimal design of flying wings and that, while static stability constraints can often be satisfied by modifying the airfoil profiles of the wing, dynamic stability constraints can require a significant change in the planform of the aircraft in order for the constraints to be satisfied.

Optimal synchronization in space

NASA Astrophysics Data System (ADS)

Brede, Markus

2010-02-01

In this Rapid Communication we investigate spatially constrained networks that realize optimal synchronization properties. After arguing that spatial constraints can be imposed by limiting the amount of “wire” available to connect nodes distributed in space, we use numerical optimization methods to construct networks that realize different trade offs between optimal synchronization and spatial constraints. Over a large range of parameters such optimal networks are found to have a link length distribution characterized by power-law tails P(l)∝l-α , with exponents α increasing as the networks become more constrained in space. It is also shown that the optimal networks, which constitute a particular type of small world network, are characterized by the presence of nodes of distinctly larger than average degree around which long-distance links are centered.

Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2018-05-01

Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

A chance constraint estimation approach to optimizing resource management under uncertainty

Treesearch

Michael Bevers

2007-01-01

Chance-constrained optimization is an important method for managing risk arising from random variations in natural resource systems, but the probabilistic formulations often pose mathematical programming problems that cannot be solved with exact methods. A heuristic estimation method for these problems is presented that combines a formulation for order statistic...

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

Robust Path Planning and Feedback Design Under Stochastic Uncertainty

NASA Technical Reports Server (NTRS)

Blackmore, Lars

2008-01-01

Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.

Parameterized LMI Based Diagonal Dominance Compensator Study for Polynomial Linear Parameter Varying System

NASA Astrophysics Data System (ADS)

Han, Xiaobao; Li, Huacong; Jia, Qiusheng

2017-12-01

For dynamic decoupling of polynomial linear parameter varying(PLPV) system, a robust dominance pre-compensator design method is given. The parameterized precompensator design problem is converted into an optimal problem constrained with parameterized linear matrix inequalities(PLMI) by using the conception of parameterized Lyapunov function(PLF). To solve the PLMI constrained optimal problem, the precompensator design problem is reduced into a normal convex optimization problem with normal linear matrix inequalities (LMI) constraints on a new constructed convex polyhedron. Moreover, a parameter scheduling pre-compensator is achieved, which satisfies robust performance and decoupling performances. Finally, the feasibility and validity of the robust diagonal dominance pre-compensator design method are verified by the numerical simulation on a turbofan engine PLPV model.

A subgradient approach for constrained binary optimization via quantum adiabatic evolution

NASA Astrophysics Data System (ADS)

Karimi, Sahar; Ronagh, Pooya

2017-08-01

Outer approximation method has been proposed for solving the Lagrangian dual of a constrained binary quadratic programming problem via quantum adiabatic evolution in the literature. This should be an efficient prescription for solving the Lagrangian dual problem in the presence of an ideally noise-free quantum adiabatic system. However, current implementations of quantum annealing systems demand methods that are efficient at handling possible sources of noise. In this paper, we consider a subgradient method for finding an optimal primal-dual pair for the Lagrangian dual of a constrained binary polynomial programming problem. We then study the quadratic stable set (QSS) problem as a case study. We see that this method applied to the QSS problem can be viewed as an instance-dependent penalty-term approach that avoids large penalty coefficients. Finally, we report our experimental results of using the D-Wave 2X quantum annealer and conclude that our approach helps this quantum processor to succeed more often in solving these problems compared to the usual penalty-term approaches.

PI controller design of a wind turbine: evaluation of the pole-placement method and tuning using constrained optimization

NASA Astrophysics Data System (ADS)

Mirzaei, Mahmood; Tibaldi, Carlo; Hansen, Morten H.

2016-09-01

PI/PID controllers are the most common wind turbine controllers. Normally a first tuning is obtained using methods such as pole-placement or Ziegler-Nichols and then extensive aeroelastic simulations are used to obtain the best tuning in terms of regulation of the outputs and reduction of the loads. In the traditional tuning approaches, the properties of different open loop and closed loop transfer functions of the system are not normally considered. In this paper, an assessment of the pole-placement tuning method is presented based on robustness measures. Then a constrained optimization setup is suggested to automatically tune the wind turbine controller subject to robustness constraints. The properties of the system such as the maximum sensitivity and complementary sensitivity functions (Ms and Mt ), along with some of the responses of the system, are used to investigate the controller performance and formulate the optimization problem. The cost function is the integral absolute error (IAE) of the rotational speed from a disturbance modeled as a step in wind speed. Linearized model of the DTU 10-MW reference wind turbine is obtained using HAWCStab2. Thereafter, the model is reduced with model order reduction. The trade-off curves are given to assess the tunings of the poles- placement method and a constrained optimization problem is solved to find the best tuning.

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn; Lin, Guang, E-mail: lin491@purdue.edu; Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by threemore » steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.« less

Pseudo-time methods for constrained optimization problems governed by PDE

NASA Technical Reports Server (NTRS)

Taasan, Shlomo

1995-01-01

In this paper we present a novel method for solving optimization problems governed by partial differential equations. Existing methods are gradient information in marching toward the minimum, where the constrained PDE is solved once (sometimes only approximately) per each optimization step. Such methods can be viewed as a marching techniques on the intersection of the state and costate hypersurfaces while improving the residuals of the design equations per each iteration. In contrast, the method presented here march on the design hypersurface and at each iteration improve the residuals of the state and costate equations. The new method is usually much less expensive per iteration step since, in most problems of practical interest, the design equation involves much less unknowns that that of either the state or costate equations. Convergence is shown using energy estimates for the evolution equations governing the iterative process. Numerical tests show that the new method allows the solution of the optimization problem in a cost of solving the analysis problems just a few times, independent of the number of design parameters. The method can be applied using single grid iterations as well as with multigrid solvers.

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

DTIC Science & Technology

2012-08-17

the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth...method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chie y but not...significantly outperforming several state-of-the-art solvers on most tested problems. The resulting MATLAB solver, called TVAL3, has been posted online [23]. 2

Estimating contrast transfer function and associated parameters by constrained non-linear optimization.

PubMed

Yang, C; Jiang, W; Chen, D-H; Adiga, U; Ng, E G; Chiu, W

2009-03-01

The three-dimensional reconstruction of macromolecules from two-dimensional single-particle electron images requires determination and correction of the contrast transfer function (CTF) and envelope function. A computational algorithm based on constrained non-linear optimization is developed to estimate the essential parameters in the CTF and envelope function model simultaneously and automatically. The application of this estimation method is demonstrated with focal series images of amorphous carbon film as well as images of ice-embedded icosahedral virus particles suspended across holes.

Domain decomposition in time for PDE-constrained optimization

DOE PAGES

Barker, Andrew T.; Stoll, Martin

2015-08-28

Here, PDE-constrained optimization problems have a wide range of applications, but they lead to very large and ill-conditioned linear systems, especially if the problems are time dependent. In this paper we outline an approach for dealing with such problems by decomposing them in time and applying an additive Schwarz preconditioner in time, so that we can take advantage of parallel computers to deal with the very large linear systems. We then illustrate the performance of our method on a variety of problems.

UAV path planning using artificial potential field method updated by optimal control theory

NASA Astrophysics Data System (ADS)

Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long

2016-04-01

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

High resolution near on-axis digital holography using constrained optimization approach with faster convergence

NASA Astrophysics Data System (ADS)

Pandiyan, Vimal Prabhu; Khare, Kedar; John, Renu

2017-09-01

A constrained optimization approach with faster convergence is proposed to recover the complex object field from a near on-axis digital holography (DH). We subtract the DC from the hologram after recording the object beam and reference beam intensities separately. The DC-subtracted hologram is used to recover the complex object information using a constrained optimization approach with faster convergence. The recovered complex object field is back propagated to the image plane using the Fresnel back-propagation method. The results reported in this approach provide high-resolution images compared with the conventional Fourier filtering approach and is 25% faster than the previously reported constrained optimization approach due to the subtraction of two DC terms in the cost function. We report this approach in DH and digital holographic microscopy using the U.S. Air Force resolution target as the object to retrieve the high-resolution image without DC and twin image interference. We also demonstrate the high potential of this technique in transparent microelectrode patterned on indium tin oxide-coated glass, by reconstructing a high-resolution quantitative phase microscope image. We also demonstrate this technique by imaging yeast cells.

An efficient interior-point algorithm with new non-monotone line search filter method for nonlinear constrained programming

NASA Astrophysics Data System (ADS)

Wang, Liwei; Liu, Xinggao; Zhang, Zeyin

2017-02-01

An efficient primal-dual interior-point algorithm using a new non-monotone line search filter method is presented for nonlinear constrained programming, which is widely applied in engineering optimization. The new non-monotone line search technique is introduced to lead to relaxed step acceptance conditions and improved convergence performance. It can also avoid the choice of the upper bound on the memory, which brings obvious disadvantages to traditional techniques. Under mild assumptions, the global convergence of the new non-monotone line search filter method is analysed, and fast local convergence is ensured by second order corrections. The proposed algorithm is applied to the classical alkylation process optimization problem and the results illustrate its effectiveness. Some comprehensive comparisons to existing methods are also presented.

Fitting Prony Series To Data On Viscoelastic Materials

NASA Technical Reports Server (NTRS)

Hill, S. A.

1995-01-01

Improved method of fitting Prony series to data on viscoelastic materials involves use of least-squares optimization techniques. Based on optimization techniques yields closer correlation with data than traditional method. Involves no assumptions regarding the gamma'(sub i)s and higher-order terms, and provides for as many Prony terms as needed to represent higher-order subtleties in data. Curve-fitting problem treated as design-optimization problem and solved by use of partially-constrained-optimization techniques.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

PubMed Central

Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen

2018-01-01

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635

An adaptive finite element method for the inequality-constrained Reynolds equation

NASA Astrophysics Data System (ADS)

Gustafsson, Tom; Rajagopal, Kumbakonam R.; Stenberg, Rolf; Videman, Juha

2018-07-01

We present a stabilized finite element method for the numerical solution of cavitation in lubrication, modeled as an inequality-constrained Reynolds equation. The cavitation model is written as a variable coefficient saddle-point problem and approximated by a residual-based stabilized method. Based on our recent results on the classical obstacle problem, we present optimal a priori estimates and derive novel a posteriori error estimators. The method is implemented as a Nitsche-type finite element technique and shown in numerical computations to be superior to the usually applied penalty methods.

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

On optimal strategies in event-constrained differential games

NASA Technical Reports Server (NTRS)

Heymann, M.; Rajan, N.; Ardema, M.

1985-01-01

Combat games are formulated as zero-sum differential games with unilateral event constraints. An interior penalty function approach is employed to approximate optimal strategies for the players. The method is very attractive computationally and possesses suitable approximation and convergence properties.

Design Optimization Toolkit: Users' Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilo Valentin, Miguel Alejandro

The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLABmore » command window.« less

Two tradeoffs between economy and reliability in loss of load probability constrained unit commitment

NASA Astrophysics Data System (ADS)

Liu, Yuan; Wang, Mingqiang; Ning, Xingyao

2018-02-01

Spinning reserve (SR) should be scheduled considering the balance between economy and reliability. To address the computational intractability cursed by the computation of loss of load probability (LOLP), many probabilistic methods use simplified formulations of LOLP to improve the computational efficiency. Two tradeoffs embedded in the SR optimization model are not explicitly analyzed in these methods. In this paper, two tradeoffs including primary tradeoff and secondary tradeoff between economy and reliability in the maximum LOLP constrained unit commitment (UC) model are explored and analyzed in a small system and in IEEE-RTS System. The analysis on the two tradeoffs can help in establishing new efficient simplified LOLP formulations and new SR optimization models.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

PubMed

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

An, Y; Liang, J; Liu, W

2015-06-15

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios withmore » certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with explicit control of plan robustness. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.« less

Computer object segmentation by nonlinear image enhancement, multidimensional clustering, and geometrically constrained contour optimization

NASA Astrophysics Data System (ADS)

Bruynooghe, Michel M.

1998-04-01

In this paper, we present a robust method for automatic object detection and delineation in noisy complex images. The proposed procedure is a three stage process that integrates image segmentation by multidimensional pixel clustering and geometrically constrained optimization of deformable contours. The first step is to enhance the original image by nonlinear unsharp masking. The second step is to segment the enhanced image by multidimensional pixel clustering, using our reducible neighborhoods clustering algorithm that has a very interesting theoretical maximal complexity. Then, candidate objects are extracted and initially delineated by an optimized region merging algorithm, that is based on ascendant hierarchical clustering with contiguity constraints and on the maximization of average contour gradients. The third step is to optimize the delineation of previously extracted and initially delineated objects. Deformable object contours have been modeled by cubic splines. An affine invariant has been used to control the undesired formation of cusps and loops. Non linear constrained optimization has been used to maximize the external energy. This avoids the difficult and non reproducible choice of regularization parameters, that are required by classical snake models. The proposed method has been applied successfully to the detection of fine and subtle microcalcifications in X-ray mammographic images, to defect detection by moire image analysis, and to the analysis of microrugosities of thin metallic films. The later implementation of the proposed method on a digital signal processor associated to a vector coprocessor would allow the design of a real-time object detection and delineation system for applications in medical imaging and in industrial computer vision.

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

PubMed

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

Spacecraft inertia estimation via constrained least squares

NASA Technical Reports Server (NTRS)

Keim, Jason A.; Acikmese, Behcet A.; Shields, Joel F.

2006-01-01

This paper presents a new formulation for spacecraft inertia estimation from test data. Specifically, the inertia estimation problem is formulated as a constrained least squares minimization problem with explicit bounds on the inertia matrix incorporated as LMIs [linear matrix inequalities). The resulting minimization problem is a semidefinite optimization that can be solved efficiently with guaranteed convergence to the global optimum by readily available algorithms. This method is applied to data collected from a robotic testbed consisting of a freely rotating body. The results show that the constrained least squares approach produces more accurate estimates of the inertia matrix than standard unconstrained least squares estimation methods.

Smooth Constrained Heuristic Optimization of a Combinatorial Chemical Space

DTIC Science & Technology

2015-05-01

ARL-TR-7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...

Dynamic optimization and its relation to classical and quantum constrained systems

NASA Astrophysics Data System (ADS)

Contreras, Mauricio; Pellicer, Rely; Villena, Marcelo

2017-08-01

We study the structure of a simple dynamic optimization problem consisting of one state and one control variable, from a physicist's point of view. By using an analogy to a physical model, we study this system in the classical and quantum frameworks. Classically, the dynamic optimization problem is equivalent to a classical mechanics constrained system, so we must use the Dirac method to analyze it in a correct way. We find that there are two second-class constraints in the model: one fix the momenta associated with the control variables, and the other is a reminder of the optimal control law. The dynamic evolution of this constrained system is given by the Dirac's bracket of the canonical variables with the Hamiltonian. This dynamic results to be identical to the unconstrained one given by the Pontryagin equations, which are the correct classical equations of motion for our physical optimization problem. In the same Pontryagin scheme, by imposing a closed-loop λ-strategy, the optimality condition for the action gives a consistency relation, which is associated to the Hamilton-Jacobi-Bellman equation of the dynamic programming method. A similar result is achieved by quantizing the classical model. By setting the wave function Ψ(x , t) =e iS(x , t) in the quantum Schrödinger equation, a non-linear partial equation is obtained for the S function. For the right-hand side quantization, this is the Hamilton-Jacobi-Bellman equation, when S(x , t) is identified with the optimal value function. Thus, the Hamilton-Jacobi-Bellman equation in Bellman's maximum principle, can be interpreted as the quantum approach of the optimization problem.

State transformations and Hamiltonian structures for optimal control in discrete systems

NASA Astrophysics Data System (ADS)

Sieniutycz, S.

2006-04-01

Preserving usual definition of Hamiltonian H as the scalar product of rates and generalized momenta we investigate two basic classes of discrete optimal control processes governed by the difference rather than differential equations for the state transformation. The first class, linear in the time interval θ, secures the constancy of optimal H and satisfies a discrete Hamilton-Jacobi equation. The second class, nonlinear in θ, does not assure the constancy of optimal H and satisfies only a relationship that may be regarded as an equation of Hamilton-Jacobi type. The basic question asked is if and when Hamilton's canonical structures emerge in optimal discrete systems. For a constrained discrete control, general optimization algorithms are derived that constitute powerful theoretical and computational tools when evaluating extremum properties of constrained physical systems. The mathematical basis is Bellman's method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage optimality criterion which allows a variation of the terminal state that is otherwise fixed in Bellman's method. For systems with unconstrained intervals of the holdup time θ two powerful optimization algorithms are obtained: an unconventional discrete algorithm with a constant H and its counterpart for models nonlinear in θ. We also present the time-interval-constrained extension of the second algorithm. The results are general; namely, one arrives at: discrete canonical equations of Hamilton, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory, along with basic results of variational calculus. A vast spectrum of applications and an example are briefly discussed with particular attention paid to models nonlinear in the time interval θ.

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

A framework to determine the locations of the environmental monitoring in an estuary of the Yellow Sea.

PubMed

Kim, Nam-Hoon; Hwang, Jin Hwan; Cho, Jaegab; Kim, Jae Seong

2018-06-04

The characteristics of an estuary are determined by various factors as like as tide, wave, river discharge, etc. which also control the water quality of the estuary. Therefore, detecting the changes of characteristics is critical in managing the environmental qualities and pollution and so the locations of monitoring should be selected carefully. The present study proposes a framework to deploy the monitoring systems based on a graphical method of the spatial and temporal optimizations. With the well-validated numerical simulation results, the monitoring locations are determined to capture the changes of water qualities and pollutants depending on the variations of tide, current and freshwater discharge. The deployment strategy to find the appropriate monitoring locations is designed with the constrained optimization method, which finds solutions by constraining the objective function into the feasible regions. The objective and constrained functions are constructed with the interpolation technique such as objective analysis. Even with the smaller number of the monitoring locations, the present method performs well equivalently to the arbitrarily and evenly deployed monitoring system. Copyright © 2018 Elsevier Ltd. All rights reserved.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Nonlinear model predictive control of a wave energy converter based on differential flatness parameterisation

NASA Astrophysics Data System (ADS)

Li, Guang

2017-01-01

This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC model. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the continuous constrained optimal control of WEC using pseudospectral method. The alleviation of computational burden using this approach helps to promote an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. Numerical simulations demonstrate the efficacy of this approach.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2012-01-01

In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

A method to stabilize linear systems using eigenvalue gradient information

NASA Technical Reports Server (NTRS)

Wieseman, C. D.

1985-01-01

Formal optimization methods and eigenvalue gradient information are used to develop a stabilizing control law for a closed loop linear system that is initially unstable. The method was originally formulated by using direct, constrained optimization methods with the constraints being the real parts of the eigenvalues. However, because of problems in trying to achieve stabilizing control laws, the problem was reformulated to be solved differently. The method described uses the Davidon-Fletcher-Powell minimization technique to solve an indirect, constrained minimization problem in which the performance index is the Kreisselmeier-Steinhauser function of the real parts of all the eigenvalues. The method is applied successfully to solve two different problems: the determination of a fourth-order control law stabilizes a single-input single-output active flutter suppression system and the determination of a second-order control law for a multi-input multi-output lateral-directional flight control system. Various sets of design variables and initial starting points were chosen to show the robustness of the method.

Optimal Control of Evolution Mixed Variational Inclusions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx

2013-12-15

Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Deterministic Reconfigurable Control Design for the X-33 Vehicle

NASA Technical Reports Server (NTRS)

Wagner, Elaine A.; Burken, John J.; Hanson, Curtis E.; Wohletz, Jerry M.

1998-01-01

In the event of a control surface failure, the purpose of a reconfigurable control system is to redistribute the control effort among the remaining working surfaces such that satisfactory stability and performance are retained. Four reconfigurable control design methods were investigated for the X-33 vehicle: Redistributed Pseudo-Inverse, General Constrained Optimization, Automated Failure Dependent Gain Schedule, and an Off-line Nonlinear General Constrained Optimization. The Off-line Nonlinear General Constrained Optimization approach was chosen for implementation on the X-33. Two example failures are shown, a right outboard elevon jam at 25 deg. at a Mach 3 entry condition, and a left rudder jam at 30 degrees. Note however, that reconfigurable control laws have been designed for the entire flight envelope. Comparisons between responses with the nominal controller and reconfigurable controllers show the benefits of reconfiguration. Single jam aerosurface failures were considered, and failure detection and identification is considered accomplished in the actuator controller. The X-33 flight control system will incorporate reconfigurable flight control in the baseline system.

A Numerical Comparison of Barrier and Modified Barrier Methods for Large-Scale Bound-Constrained Optimization

NASA Technical Reports Server (NTRS)

Nash, Stephen G.; Polyak, R.; Sofer, Ariela

1994-01-01

When a classical barrier method is applied to the solution of a nonlinear programming problem with inequality constraints, the Hessian matrix of the barrier function becomes increasingly ill-conditioned as the solution is approached. As a result, it may be desirable to consider alternative numerical algorithms. We compare the performance of two methods motivated by barrier functions. The first is a stabilized form of the classical barrier method, where a numerically stable approximation to the Newton direction is used when the barrier parameter is small. The second is a modified barrier method where a barrier function is applied to a shifted form of the problem, and the resulting barrier terms are scaled by estimates of the optimal Lagrange multipliers. The condition number of the Hessian matrix of the resulting modified barrier function remains bounded as the solution to the constrained optimization problem is approached. Both of these techniques can be used in the context of a truncated-Newton method, and hence can be applied to large problems, as well as on parallel computers. In this paper, both techniques are applied to problems with bound constraints and we compare their practical behavior.

Research on optimal DEM cell size for 3D visualization of loess terraces

NASA Astrophysics Data System (ADS)

Zhao, Weidong; Tang, Guo'an; Ji, Bin; Ma, Lei

2009-10-01

In order to represent the complex artificial terrains like loess terraces in Shanxi Province in northwest China, a new 3D visual method namely Terraces Elevation Incremental Visual Method (TEIVM) is put forth by the authors. 406 elevation points and 14 enclosed constrained lines are sampled according to the TIN-based Sampling Method (TSM) and DEM Elevation Points and Lines Classification (DEPLC). The elevation points and constrained lines are used to construct Constrained Delaunay Triangulated Irregular Networks (CD-TINs) of the loess terraces. In order to visualize the loess terraces well by use of optimal combination of cell size and Elevation Increment Value (EIV), the CD-TINs is converted to Grid-based DEM (G-DEM) by use of different combination of cell size and EIV with linear interpolating method called Bilinear Interpolation Method (BIM). Our case study shows that the new visual method can visualize the loess terraces steps very well when the combination of cell size and EIV is reasonable. The optimal combination is that the cell size is 1 m and the EIV is 6 m. Results of case study also show that the cell size should be at least smaller than half of both the terraces average width and the average vertical offset of terraces steps for representing the planar shapes of the terraces surfaces and steps well, while the EIV also should be larger than 4.6 times of the terraces average height. The TEIVM and results above is of great significance to the highly refined visualization of artificial terrains like loess terraces.

Highly Constrained Bicyclic Scaffolds for the Discovery of Protease-Stable Peptides via mRNA Display.

PubMed

Hacker, David E; Hoinka, Jan; Iqbal, Emil S; Przytycka, Teresa M; Hartman, Matthew C T

2017-03-17

Highly constrained peptides such as the knotted peptide natural products are promising medicinal agents because of their impressive biostability and potent activity. Yet, libraries of highly constrained peptides are challenging to prepare. Here, we present a method which utilizes two robust, orthogonal chemical steps to create highly constrained bicyclic peptide libraries. This technology was optimized to be compatible with in vitro selections by mRNA display. We performed side-by-side monocyclic and bicyclic selections against a model protein (streptavidin). Both selections resulted in peptides with mid-nanomolar affinity, and the bicyclic selection yielded a peptide with remarkable protease resistance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simonetto, Andrea; Dall'Anese, Emiliano

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

Robust Airfoil Optimization to Achieve Consistent Drag Reduction Over a Mach Range

NASA Technical Reports Server (NTRS)

Li, Wu; Huyse, Luc; Padula, Sharon; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We prove mathematically that in order to avoid point-optimization at the sampled design points for multipoint airfoil optimization, the number of design points must be greater than the number of free-design variables. To overcome point-optimization at the sampled design points, a robust airfoil optimization method (called the profile optimization method) is developed and analyzed. This optimization method aims at a consistent drag reduction over a given Mach range and has three advantages: (a) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (b) there is no random airfoil shape distortion for any iterate it generates, and (c) it allows a designer to make a trade-off between a truly optimized airfoil and the amount of computing time consumed. For illustration purposes, we use the profile optimization method to solve a lift-constrained drag minimization problem for 2-D airfoil in Euler flow with 20 free-design variables. A comparison with other airfoil optimization methods is also included.

Options for Robust Airfoil Optimization under Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Li, Wu

2002-01-01

A robust optimization method is developed to overcome point-optimization at the sampled design points. This method combines the best features from several preliminary methods proposed by the authors and their colleagues. The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of spline control points as design variables yet the resulting airfoil shape does not need to be smoothed, and (3) it allows the user to make a tradeoff between the level of optimization and the amount of computing time consumed. For illustration purposes, the robust optimization method is used to solve a lift-constrained drag minimization problem for a two-dimensional (2-D) airfoil in Euler flow with 20 geometric design variables.

On the constrained minimization of smooth Kurdyka—Łojasiewicz functions with the scaled gradient projection method

NASA Astrophysics Data System (ADS)

Prato, Marco; Bonettini, Silvia; Loris, Ignace; Porta, Federica; Rebegoldi, Simone

2016-10-01

The scaled gradient projection (SGP) method is a first-order optimization method applicable to the constrained minimization of smooth functions and exploiting a scaling matrix multiplying the gradient and a variable steplength parameter to improve the convergence of the scheme. For a general nonconvex function, the limit points of the sequence generated by SGP have been proved to be stationary, while in the convex case and with some restrictions on the choice of the scaling matrix the sequence itself converges to a constrained minimum point. In this paper we extend these convergence results by showing that the SGP sequence converges to a limit point provided that the objective function satisfies the Kurdyka-Łojasiewicz property at each point of its domain and its gradient is Lipschitz continuous.

A globally convergent LCL method for nonlinear optimization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friedlander, M. P.; Saunders, M. A.; Mathematics and Computer Science

2005-01-01

For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods solve a sequence of subproblems of the form 'minimize an augmented Lagrangian function subject to linearized constraints.' Such methods converge rapidly near a solution but may not be reliable from arbitrary starting points. Nevertheless, the well-known software package MINOS has proved effective on many large problems. Its success motivates us to derive a related LCL algorithm that possesses three important properties: it is globally convergent, the subproblem constraints are always feasible, and the subproblems may be solved inexactly. The new algorithm has been implemented in Matlab, with an optionmore » to use either MINOS or SNOPT (Fortran codes) to solve the linearly constrained subproblems. Only first derivatives are required. We present numerical results on a subset of the COPS, HS, and CUTE test problems, which include many large examples. The results demonstrate the robustness and efficiency of the stabilized LCL procedure.« less

Determination of the Conservation Time of Periodicals for Optimal Shelf Maintenance of a Library.

ERIC Educational Resources Information Center

Miyamoto, Sadaaki; Nakayama, Kazuhiko

1981-01-01

Presents a method based on a constrained optimization technique that determines the time of removal of scientific periodicals from the shelf of a library. A geometrical interpretation of the theoretical result is given, and a numerical example illustrates how the technique is applicable to real bibliographic data. (FM)

A DAG Scheduling Scheme on Heterogeneous Computing Systems Using Tuple-Based Chemical Reaction Optimization

PubMed Central

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems. PMID:25143977

A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization.

PubMed

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems.

On the optimization of electromagnetic geophysical data: Application of the PSO algorithm

NASA Astrophysics Data System (ADS)

Godio, A.; Santilano, A.

2018-01-01

Particle Swarm optimization (PSO) algorithm resolves constrained multi-parameter problems and is suitable for simultaneous optimization of linear and nonlinear problems, with the assumption that forward modeling is based on good understanding of ill-posed problem for geophysical inversion. We apply PSO for solving the geophysical inverse problem to infer an Earth model, i.e. the electrical resistivity at depth, consistent with the observed geophysical data. The method doesn't require an initial model and can be easily constrained, according to external information for each single sounding. The optimization process to estimate the model parameters from the electromagnetic soundings focuses on the discussion of the objective function to be minimized. We discuss the possibility to introduce in the objective function vertical and lateral constraints, with an Occam-like regularization. A sensitivity analysis allowed us to check the performance of the algorithm. The reliability of the approach is tested on synthetic, real Audio-Magnetotelluric (AMT) and Long Period MT data. The method appears able to solve complex problems and allows us to estimate the a posteriori distribution of the model parameters.

Improving approximate-optimized effective potentials by imposing exact conditions: Theory and applications to electronic statics and dynamics

NASA Astrophysics Data System (ADS)

Kurzweil, Yair; Head-Gordon, Martin

2009-07-01

We develop a method that can constrain any local exchange-correlation potential to preserve basic exact conditions. Using the method of Lagrange multipliers, we calculate for each set of given Kohn-Sham orbitals a constraint-preserving potential which is closest to the given exchange-correlation potential. The method is applicable to both the time-dependent (TD) and independent cases. The exact conditions that are enforced for the time-independent case are Galilean covariance, zero net force and torque, and Levy-Perdew virial theorem. For the time-dependent case we enforce translational covariance, zero net force, Levy-Perdew virial theorem, and energy balance. We test our method on the exchange (only) Krieger-Li-Iafrate (xKLI) approximate-optimized effective potential for both cases. For the time-independent case, we calculated the ground state properties of some hydrogen chains and small sodium clusters for some constrained xKLI potentials and Hartree-Fock (HF) exchange. The results (total energy, Kohn-Sham eigenvalues, polarizability, and hyperpolarizability) indicate that enforcing the exact conditions is not important for these cases. On the other hand, in the time-dependent case, constraining both energy balance and zero net force yields improved results relative to TDHF calculations. We explored the electron dynamics in small sodium clusters driven by cw laser pulses. For each laser pulse we compared calculations from TD constrained xKLI, TD partially constrained xKLI, and TDHF. We found that electron dynamics such as electron ionization and moment of inertia dynamics for the constrained xKLI are most similar to the TDHF results. Also, energy conservation is better by at least one order of magnitude with respect to the unconstrained xKLI. We also discuss the problems that arise in satisfying constraints in the TD case with a non-cw driving force.

Improving approximate-optimized effective potentials by imposing exact conditions: Theory and applications to electronic statics and dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurzweil, Yair; Head-Gordon, Martin

2009-07-15

We develop a method that can constrain any local exchange-correlation potential to preserve basic exact conditions. Using the method of Lagrange multipliers, we calculate for each set of given Kohn-Sham orbitals a constraint-preserving potential which is closest to the given exchange-correlation potential. The method is applicable to both the time-dependent (TD) and independent cases. The exact conditions that are enforced for the time-independent case are Galilean covariance, zero net force and torque, and Levy-Perdew virial theorem. For the time-dependent case we enforce translational covariance, zero net force, Levy-Perdew virial theorem, and energy balance. We test our method on the exchangemore » (only) Krieger-Li-Iafrate (xKLI) approximate-optimized effective potential for both cases. For the time-independent case, we calculated the ground state properties of some hydrogen chains and small sodium clusters for some constrained xKLI potentials and Hartree-Fock (HF) exchange. The results (total energy, Kohn-Sham eigenvalues, polarizability, and hyperpolarizability) indicate that enforcing the exact conditions is not important for these cases. On the other hand, in the time-dependent case, constraining both energy balance and zero net force yields improved results relative to TDHF calculations. We explored the electron dynamics in small sodium clusters driven by cw laser pulses. For each laser pulse we compared calculations from TD constrained xKLI, TD partially constrained xKLI, and TDHF. We found that electron dynamics such as electron ionization and moment of inertia dynamics for the constrained xKLI are most similar to the TDHF results. Also, energy conservation is better by at least one order of magnitude with respect to the unconstrained xKLI. We also discuss the problems that arise in satisfying constraints in the TD case with a non-cw driving force.« less

Viscoelastic material inversion using Sierra-SD and ROL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Timothy; Aquino, Wilkins; Ridzal, Denis

2014-11-01

In this report we derive frequency-domain methods for inverse characterization of the constitutive parameters of viscoelastic materials. The inverse problem is cast in a PDE-constrained optimization framework with efficient computation of gradients and Hessian vector products through matrix free operations. The abstract optimization operators for first and second derivatives are derived from first principles. Various methods from the Rapid Optimization Library (ROL) are tested on the viscoelastic inversion problem. The methods described herein are applied to compute the viscoelastic bulk and shear moduli of a foam block model, which was recently used in experimental testing for viscoelastic property characterization.

Numerical methods for the inverse problem of density functional theory

DOE PAGES

Jensen, Daniel S.; Wasserman, Adam

2017-07-17

Here, the inverse problem of Kohn–Sham density functional theory (DFT) is often solved in an effort to benchmark and design approximate exchange-correlation potentials. The forward and inverse problems of DFT rely on the same equations but the numerical methods for solving each problem are substantially different. We examine both problems in this tutorial with a special emphasis on the algorithms and error analysis needed for solving the inverse problem. Two inversion methods based on partial differential equation constrained optimization and constrained variational ideas are introduced. We compare and contrast several different inversion methods applied to one-dimensional finite and periodic modelmore » systems.« less

Numerical methods for the inverse problem of density functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, Daniel S.; Wasserman, Adam

Here, the inverse problem of Kohn–Sham density functional theory (DFT) is often solved in an effort to benchmark and design approximate exchange-correlation potentials. The forward and inverse problems of DFT rely on the same equations but the numerical methods for solving each problem are substantially different. We examine both problems in this tutorial with a special emphasis on the algorithms and error analysis needed for solving the inverse problem. Two inversion methods based on partial differential equation constrained optimization and constrained variational ideas are introduced. We compare and contrast several different inversion methods applied to one-dimensional finite and periodic modelmore » systems.« less

CSOLNP: Numerical Optimization Engine for Solving Non-linearly Constrained Problems.

PubMed

Zahery, Mahsa; Maes, Hermine H; Neale, Michael C

2017-08-01

We introduce the optimizer CSOLNP, which is a C++ implementation of the R package RSOLNP (Ghalanos & Theussl, 2012, Rsolnp: General non-linear optimization using augmented Lagrange multiplier method. R package version, 1) alongside some improvements. CSOLNP solves non-linearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. CSOLNP, NPSOL (a very popular implementation of SQP method in FORTRAN (Gill et al., 1986, User's guide for NPSOL (version 4.0): A Fortran package for nonlinear programming (No. SOL-86-2). Stanford, CA: Stanford University Systems Optimization Laboratory), and SLSQP (another SQP implementation available as part of the NLOPT collection (Johnson, 2014, The NLopt nonlinear-optimization package. Retrieved from http://ab-initio.mit.edu/nlopt)) are three optimizers available in OpenMx package. These optimizers are compared in terms of runtimes, final objective values, and memory consumption. A Monte Carlo analysis of the performance of the optimizers was performed on ordinal and continuous models with five variables and one or two factors. While the relative difference between the objective values is less than 0.5%, CSOLNP is in general faster than NPSOL and SLSQP for ordinal analysis. As for continuous data, none of the optimizers performs consistently faster than the others. In terms of memory usage, we used Valgrind's heap profiler tool, called Massif, on one-factor threshold models. CSOLNP and NPSOL consume the same amount of memory, while SLSQP uses 71 MB more memory than the other two optimizers.

Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables

DOE Office of Scientific and Technical Information (OSTI.GOV)

DallAnese, Emiliano; Baker, Kyri; Summers, Tyler

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrarymore » distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.« less

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

NASA Astrophysics Data System (ADS)

Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

2018-05-01

High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

Incorporating Objective Function Information Into the Feasibility Rule for Constrained Evolutionary Optimization.

PubMed

Wang, Yong; Wang, Bing-Chuan; Li, Han-Xiong; Yen, Gary G

2016-12-01

When solving constrained optimization problems by evolutionary algorithms, an important issue is how to balance constraints and objective function. This paper presents a new method to address the above issue. In our method, after generating an offspring for each parent in the population by making use of differential evolution (DE), the well-known feasibility rule is used to compare the offspring and its parent. Since the feasibility rule prefers constraints to objective function, the objective function information has been exploited as follows: if the offspring cannot survive into the next generation and if the objective function value of the offspring is better than that of the parent, then the offspring is stored into a predefined archive. Subsequently, the individuals in the archive are used to replace some individuals in the population according to a replacement mechanism. Moreover, a mutation strategy is proposed to help the population jump out of a local optimum in the infeasible region. Note that, in the replacement mechanism and the mutation strategy, the comparison of individuals is based on objective function. In addition, the information of objective function has also been utilized to generate offspring in DE. By the above processes, this paper achieves an effective balance between constraints and objective function in constrained evolutionary optimization. The performance of our method has been tested on two sets of benchmark test functions, namely, 24 test functions at IEEE CEC2006 and 18 test functions with 10-D and 30-D at IEEE CEC2010. The experimental results have demonstrated that our method shows better or at least competitive performance against other state-of-the-art methods. Furthermore, the advantage of our method increases with the increase of the number of decision variables.

Improving Free-Piston Stirling Engine Specific Power

NASA Technical Reports Server (NTRS)

Briggs, Maxwell Henry

2014-01-01

This work uses analytical methods to demonstrate the potential benefits of optimizing piston and/or displacer motion in a Stirling Engine. Isothermal analysis was used to show the potential benefits of ideal motion in ideal Stirling engines. Nodal analysis is used to show that ideal piston and displacer waveforms are not optimal in real Stirling engines. Constrained optimization was used to identify piston and displacer waveforms that increase Stirling engine specific power.

Improving Free-Piston Stirling Engine Specific Power

NASA Technical Reports Server (NTRS)

Briggs, Maxwell H.

2015-01-01

This work uses analytical methods to demonstrate the potential benefits of optimizing piston and/or displacer motion in a Stirling engine. Isothermal analysis was used to show the potential benefits of ideal motion in ideal Stirling engines. Nodal analysis is used to show that ideal piston and displacer waveforms are not optimal in real Stirling engines. Constrained optimization was used to identify piston and displacer waveforms that increase Stirling engine specific power.

Computing global minimizers to a constrained B-spline image registration problem from optimal l1 perturbations to block match data

PubMed Central

Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas

2014-01-01

Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations. PMID:24694135

A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds

NASA Technical Reports Server (NTRS)

Lewis, Robert Michael; Torczon, Virginia

1998-01-01

We give a pattern search adaptation of an augmented Lagrangian method due to Conn, Gould, and Toint. The algorithm proceeds by successive bound constrained minimization of an augmented Lagrangian. In the pattern search adaptation we solve this subproblem approximately using a bound constrained pattern search method. The stopping criterion proposed by Conn, Gould, and Toint for the solution of this subproblem requires explicit knowledge of derivatives. Such information is presumed absent in pattern search methods; however, we show how we can replace this with a stopping criterion based on the pattern size in a way that preserves the convergence properties of the original algorithm. In this way we proceed by successive, inexact, bound constrained minimization without knowing exactly how inexact the minimization is. So far as we know, this is the first provably convergent direct search method for general nonlinear programming.

Conditional Entropy-Constrained Residual VQ with Application to Image Coding

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Chung, Wilson C.; Smith, Mark J. T.

1996-01-01

This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.

A greedy algorithm for species selection in dimension reduction of combustion chemistry

NASA Astrophysics Data System (ADS)

Hiremath, Varun; Ren, Zhuyin; Pope, Stephen B.

2010-09-01

Computational calculations of combustion problems involving large numbers of species and reactions with a detailed description of the chemistry can be very expensive. Numerous dimension reduction techniques have been developed in the past to reduce the computational cost. In this paper, we consider the rate controlled constrained-equilibrium (RCCE) dimension reduction method, in which a set of constrained species is specified. For a given number of constrained species, the 'optimal' set of constrained species is that which minimizes the dimension reduction error. The direct determination of the optimal set is computationally infeasible, and instead we present a greedy algorithm which aims at determining a 'good' set of constrained species; that is, one leading to near-minimal dimension reduction error. The partially-stirred reactor (PaSR) involving methane premixed combustion with chemistry described by the GRI-Mech 1.2 mechanism containing 31 species is used to test the algorithm. Results on dimension reduction errors for different sets of constrained species are presented to assess the effectiveness of the greedy algorithm. It is shown that the first four constrained species selected using the proposed greedy algorithm produce lower dimension reduction error than constraints on the major species: CH4, O2, CO2 and H2O. It is also shown that the first ten constrained species selected using the proposed greedy algorithm produce a non-increasing dimension reduction error with every additional constrained species; and produce the lowest dimension reduction error in many cases tested over a wide range of equivalence ratios, pressures and initial temperatures.

Weighted mining of massive collections of [Formula: see text]-values by convex optimization.

PubMed

Dobriban, Edgar

2018-06-01

Researchers in data-rich disciplines-think of computational genomics and observational cosmology-often wish to mine large bodies of [Formula: see text]-values looking for significant effects, while controlling the false discovery rate or family-wise error rate. Increasingly, researchers also wish to prioritize certain hypotheses, for example, those thought to have larger effect sizes, by upweighting, and to impose constraints on the underlying mining, such as monotonicity along a certain sequence. We introduce Princessp , a principled method for performing weighted multiple testing by constrained convex optimization. Our method elegantly allows one to prioritize certain hypotheses through upweighting and to discount others through downweighting, while constraining the underlying weights involved in the mining process. When the [Formula: see text]-values derive from monotone likelihood ratio families such as the Gaussian means model, the new method allows exact solution of an important optimal weighting problem previously thought to be non-convex and computationally infeasible. Our method scales to massive data set sizes. We illustrate the applications of Princessp on a series of standard genomics data sets and offer comparisons with several previous 'standard' methods. Princessp offers both ease of operation and the ability to scale to extremely large problem sizes. The method is available as open-source software from github.com/dobriban/pvalue_weighting_matlab (accessed 11 October 2017).

Constraining the braneworld with gravitational wave observations.

PubMed

McWilliams, Sean T

2010-04-09

Some braneworld models may have observable consequences that, if detected, would validate a requisite element of string theory. In the infinite Randall-Sundrum model (RS2), the AdS radius of curvature, l, of the extra dimension supports a single bound state of the massless graviton on the brane, thereby reproducing Newtonian gravity in the weak-field limit. However, using the AdS/CFT correspondence, it has been suggested that one possible consequence of RS2 is an enormous increase in Hawking radiation emitted by black holes. We utilize this possibility to derive two novel methods for constraining l via gravitational wave measurements. We show that the EMRI event rate detected by LISA can constrain l at the approximately 1 microm level for optimal cases, while the observation of a single galactic black hole binary with LISA results in an optimal constraint of l < or = 5 microm.

Constraining the Braneworld with Gravitational Wave Observations

NASA Technical Reports Server (NTRS)

McWilliams, Sean T.

2011-01-01

Some braneworld models may have observable consequences that, if detected, would validate a requisite element of string theory. In the infinite Randall-Sundrum model (RS2), the AdS radius of curvature, L, of the extra dimension supports a single bound state of the massless graviton on the brane, thereby reproducing Newtonian gravity in the weak-field limit. However, using the AdS/CFT correspondence, it has been suggested that one possible consequence of RS2 is an enormous increase in Hawking radiation emitted by black holes. We utilize this possibility to derive two novel methods for constraining L via gravitational wave measurements. We show that the EMRI event rate detected by LISA can constrain L at the approximately 1 micron level for optimal cases, while the observation of a single galactic black hole binary with LISA results in an optimal constraint of L less than or equal to 5 microns.

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

PubMed

Xu, Jiuping; Feng, Cuiying

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.

Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project

PubMed Central

Xu, Jiuping

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708

An Experimental Comparison of Similarity Assessment Measures for 3D Models on Constrained Surface Deformation

NASA Astrophysics Data System (ADS)

Quan, Lulin; Yang, Zhixin

2010-05-01

To address the issues in the area of design customization, this paper expressed the specification and application of the constrained surface deformation, and reported the experimental performance comparison of three prevail effective similarity assessment algorithms on constrained surface deformation domain. Constrained surface deformation becomes a promising method that supports for various downstream applications of customized design. Similarity assessment is regarded as the key technology for inspecting the success of new design via measuring the difference level between the deformed new design and the initial sample model, and indicating whether the difference level is within the limitation. According to our theoretical analysis and pre-experiments, three similarity assessment algorithms are suitable for this domain, including shape histogram based method, skeleton based method, and U system moment based method. We analyze their basic functions and implementation methodologies in detail, and do a series of experiments on various situations to test their accuracy and efficiency using precision-recall diagram. Shoe model is chosen as an industrial example for the experiments. It shows that shape histogram based method gained an optimal performance in comparison. Based on the result, we proposed a novel approach that integrating surface constrains and shape histogram description with adaptive weighting method, which emphasize the role of constrains during the assessment. The limited initial experimental result demonstrated that our algorithm outperforms other three algorithms. A clear direction for future development is also drawn at the end of the paper.

A general-purpose optimization program for engineering design

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.; Sugimoto, H.

1986-01-01

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis) is a FORTRAN program for nonlinear constrained (or unconstrained) function minimization. The optimization process is segmented into three levels: Strategy, Optimizer, and One-dimensional search. At each level, several options are available so that a total of nearly 100 possible combinations can be created. An example of available combinations is the Augmented Lagrange Multiplier method, using the BFGS variable metric unconstrained minimization together with polynomial interpolation for the one-dimensional search.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

NASA Astrophysics Data System (ADS)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Efficient Transition State Optimization of Periodic Structures through Automated Relaxed Potential Energy Surface Scans.

PubMed

Plessow, Philipp N

2018-02-13

This work explores how constrained linear combinations of bond lengths can be used to optimize transition states in periodic structures. Scanning of constrained coordinates is a standard approach for molecular codes with localized basis functions, where a full set of internal coordinates is used for optimization. Common plane wave-codes for periodic boundary conditions almost exlusively rely on Cartesian coordinates. An implementation of constrained linear combinations of bond lengths with Cartesian coordinates is described. Along with an optimization of the value of the constrained coordinate toward the transition states, this allows transition optimization within a single calculation. The approach is suitable for transition states that can be well described in terms of broken and formed bonds. In particular, the implementation is shown to be effective and efficient in the optimization of transition states in zeolite-catalyzed reactions, which have high relevance in industrial processes.

A Comparison of Trajectory Optimization Methods for the Impulsive Minimum Fuel Rendezvous Problem

NASA Technical Reports Server (NTRS)

Hughes, Steven P.; Mailhe, Laurie M.; Guzman, Jose J.

2002-01-01

In this paper we present a comparison of optimization approaches to the minimum fuel rendezvous problem. Both indirect and direct methods are compared for a variety of test cases. The indirect approach is based on primer vector theory. The direct approaches are implemented numerically and include Sequential Quadratic Programming (SQP), Quasi-Newton, Simplex, Genetic Algorithms, and Simulated Annealing. Each method is applied to a variety of test cases including, circular to circular coplanar orbits, LEO to GEO, and orbit phasing in highly elliptic orbits. We also compare different constrained optimization routines on complex orbit rendezvous problems with complicated, highly nonlinear constraints.

Statistical mechanics of budget-constrained auctions

NASA Astrophysics Data System (ADS)

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-07-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

Computational alternatives to obtain time optimal jet engine control. M.S. Thesis

NASA Technical Reports Server (NTRS)

Basso, R. J.; Leake, R. J.

1976-01-01

Two computational methods to determine an open loop time optimal control sequence for a simple single spool turbojet engine are described by a set of nonlinear differential equations. Both methods are modifications of widely accepted algorithms which can solve fixed time unconstrained optimal control problems with a free right end. Constrained problems to be considered have fixed right ends and free time. Dynamic programming is defined on a standard problem and it yields a successive approximation solution to the time optimal problem of interest. A feedback control law is obtained and it is then used to determine the corresponding open loop control sequence. The Fletcher-Reeves conjugate gradient method has been selected for adaptation to solve a nonlinear optimal control problem with state variable and control constraints.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Mass-based design and optimization of wave rotors for gas turbine engine enhancement

NASA Astrophysics Data System (ADS)

Chan, S.; Liu, H.

2017-03-01

An analytic method aiming at mass properties was developed for the preliminary design and optimization of wave rotors. In the present method, we introduce the mass balance principle into the design and thus can predict and optimize the mass qualities as well as the performance of wave rotors. A dedicated least-square method with artificial weighting coefficients was developed to solve the over-constrained system in the mass-based design. This method and the adoption of the coefficients were validated by numerical simulation. Moreover, the problem of fresh air exhaustion (FAE) was put forward and analyzed, and exhaust gas recirculation (EGR) was investigated. Parameter analyses and optimization elucidated which designs would not only achieve the best performance, but also operate with minimum EGR and no FAE.

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

PubMed

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

Trajectory Design Strategies for the NGST L2 Libration Point Mission

NASA Technical Reports Server (NTRS)

Folta, David; Cooley, Steven; Howell, Kathleen; Bauer, Frank H.

2001-01-01

The Origins' Next Generation Space Telescope (NGST) trajectory design is addressed in light of improved methods for attaining constrained orbit parameters and their control at the exterior collinear libration point, L2. The use of a dynamical systems approach, state-space equations for initial libration orbit control, and optimization to achieve constrained orbit parameters are emphasized. The NGST trajectory design encompasses a direct transfer and orbit maintenance under a constant acceleration. A dynamical systems approach can be used to provide a biased orbit and stationkeeping maintenance method that incorporates the constraint of a single axis correction scheme.

A Method to Constrain Genome-Scale Models with 13C Labeling Data

PubMed Central

García Martín, Héctor; Kumar, Vinay Satish; Weaver, Daniel; Ghosh, Amit; Chubukov, Victor; Mukhopadhyay, Aindrila; Arkin, Adam; Keasling, Jay D.

2015-01-01

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems. PMID:26379153

Synthesis of aircraft structures using integrated design and analysis methods

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Goetz, R. C.

1978-01-01

A systematic research is reported to develop and validate methods for structural sizing of an airframe designed with the use of composite materials and active controls. This research program includes procedures for computing aeroelastic loads, static and dynamic aeroelasticity, analysis and synthesis of active controls, and optimization techniques. Development of the methods is concerned with the most effective ways of integrating and sequencing the procedures in order to generate structural sizing and the associated active control system, which is optimal with respect to a given merit function constrained by strength and aeroelasticity requirements.

Interior point techniques for LP and NLP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evtushenko, Y.

By using surjective mapping the initial constrained optimization problem is transformed to a problem in a new space with only equality constraints. For the numerical solution of the latter problem we use the generalized gradient-projection method and Newton`s method. After inverse transformation to the initial space we obtain the family of numerical methods for solving optimization problems with equality and inequality constraints. In the linear programming case after some simplification we obtain Dikin`s algorithm, affine scaling algorithm and generalized primal dual interior point linear programming algorithm.

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

An effective parameter optimization with radiation balance constraints in the CAM5

NASA Astrophysics Data System (ADS)

Wu, L.; Zhang, T.; Qin, Y.; Lin, Y.; Xue, W.; Zhang, M.

2017-12-01

Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. Traditional parameter tuning methods are mostly unconstrained optimization, leading to the simulation results with optimal parameters may not meet the conditions that models have to keep. In this study, the radiation balance constraint is taken as an example, which is involved in the automatic parameter optimization procedure. The Lagrangian multiplier method is used to solve this optimization problem with constrains. In our experiment, we use CAM5 atmosphere model under 5-yr AMIP simulation with prescribed seasonal climatology of SST and sea ice. We consider the synthesized metrics using global means of radiation, precipitation, relative humidity, and temperature as the goal of optimization, and simultaneously consider the conditions that FLUT and FSNTOA should satisfy as constraints. The global average of the output variables FLUT and FSNTOA are set to be approximately equal to 240 Wm-2 in CAM5. Experiment results show that the synthesized metrics is 13.6% better than the control run. At the same time, both FLUT and FSNTOA are close to the constrained conditions. The FLUT condition is well satisfied, which is obviously better than the average annual FLUT obtained with the default parameters. The FSNTOA has a slight deviation from the observed value, but the relative error is less than 7.7‰.

An all-at-once reduced Hessian SQP scheme for aerodynamic design optimization

NASA Technical Reports Server (NTRS)

Feng, Dan; Pulliam, Thomas H.

1995-01-01

This paper introduces a computational scheme for solving a class of aerodynamic design problems that can be posed as nonlinear equality constrained optimizations. The scheme treats the flow and design variables as independent variables, and solves the constrained optimization problem via reduced Hessian successive quadratic programming. It updates the design and flow variables simultaneously at each iteration and allows flow variables to be infeasible before convergence. The solution of an adjoint flow equation is never needed. In addition, a range space basis is chosen so that in a certain sense the 'cross term' ignored in reduced Hessian SQP methods is minimized. Numerical results for a nozzle design using the quasi-one-dimensional Euler equations show that this scheme is computationally efficient and robust. The computational cost of a typical nozzle design is only a fraction more than that of the corresponding analysis flow calculation. Superlinear convergence is also observed, which agrees with the theoretical properties of this scheme. All optimal solutions are obtained by starting far away from the final solution.

Optimization of constrained density functional theory

NASA Astrophysics Data System (ADS)

O'Regan, David D.; Teobaldi, Gilberto

2016-07-01

Constrained density functional theory (cDFT) is a versatile electronic structure method that enables ground-state calculations to be performed subject to physical constraints. It thereby broadens their applicability and utility. Automated Lagrange multiplier optimization is necessary for multiple constraints to be applied efficiently in cDFT, for it to be used in tandem with geometry optimization, or with molecular dynamics. In order to facilitate this, we comprehensively develop the connection between cDFT energy derivatives and response functions, providing a rigorous assessment of the uniqueness and character of cDFT stationary points while accounting for electronic interactions and screening. In particular, we provide a nonperturbative proof that stable stationary points of linear density constraints occur only at energy maxima with respect to their Lagrange multipliers. We show that multiple solutions, hysteresis, and energy discontinuities may occur in cDFT. Expressions are derived, in terms of convenient by-products of cDFT optimization, for quantities such as the dielectric function and a condition number quantifying ill definition in multiple constraint cDFT.

Publications | Grid Modernization | NREL

Science.gov Websites

Photovoltaics: Trajectories and Challenges Cover of Efficient Relaxations for Joint Chance Constrained AC Optimal Power Flow publication Efficient Relaxations for Joint Chance Constrained AC Optimal Power Flow

A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice

PubMed Central

Gao, Yuan; Zhou, Weigui; Ao, Hong; Chu, Jian; Zhou, Quan; Zhou, Bo; Wang, Kang; Li, Yi; Xue, Peng

2016-01-01

With the increasing demands for better transmission speed and robust quality of service (QoS), the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT) scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs) equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users). A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives. PMID:27077865

An Optimization-based Atomistic-to-Continuum Coupling Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olson, Derek; Bochev, Pavel B.; Luskin, Mitchell

2014-08-21

In this paper, we present a new optimization-based method for atomistic-to-continuum (AtC) coupling. The main idea is to cast the latter as a constrained optimization problem with virtual Dirichlet controls on the interfaces between the atomistic and continuum subdomains. The optimization objective is to minimize the error between the atomistic and continuum solutions on the overlap between the two subdomains, while the atomistic and continuum force balance equations provide the constraints. Separation, rather then blending of the atomistic and continuum problems, and their subsequent use as constraints in the optimization problem distinguishes our approach from the existing AtC formulations. Finally,more » we present and analyze the method in the context of a one-dimensional chain of atoms modeled using a linearized two-body potential with next-nearest neighbor interactions.« less

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

PubMed

Heydari, Ali; Balakrishnan, Sivasubramanya N

2013-01-01

To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

Direct Method Transcription for a Human-Class Translunar Injection Trajectory Optimization

NASA Technical Reports Server (NTRS)

Witzberger, Kevin E.; Zeiler, Tom

2012-01-01

This paper presents a new trajectory optimization software package developed in the framework of a low-to-high fidelity 3 degrees-of-freedom (DOF)/6-DOF vehicle simulation program named Mission Analysis Simulation Tool in Fortran (MASTIF) and its application to a translunar trajectory optimization problem. The functionality of the developed optimization package is implemented as a new "mode" in generalized settings to make it applicable for a general trajectory optimization problem. In doing so, a direct optimization method using collocation is employed for solving the problem. Trajectory optimization problems in MASTIF are transcribed to a constrained nonlinear programming (NLP) problem and solved with SNOPT, a commercially available NLP solver. A detailed description of the optimization software developed is provided as well as the transcription specifics for the translunar injection (TLI) problem. The analysis includes a 3-DOF trajectory TLI optimization and a 3-DOF vehicle TLI simulation using closed-loop guidance.

Role of slack variables in quasi-Newton methods for constrained optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tapia, R.A.

In constrained optimization the technique of converting an inequality constraint into an equality constraint by the addition of a squared slack variable is well known but rarely used. In choosing an active constraint philosophy over the slack variable approach, researchers quickly justify their choice with the standard criticisms: the slack variable approach increases the dimension of the problem, is numerically unstable, and gives rise to singular systems. It is shown that these criticisms of the slack variable approach need not apply and the two seemingly distinct approaches are actually very closely related. In fact, the squared slack variable formulation canmore » be used to develop a superior and more comprehensive active constraint philosophy.« less

Exact and explicit optimal solutions for trajectory planning and control of single-link flexible-joint manipulators

NASA Technical Reports Server (NTRS)

Chen, Guanrong

1991-01-01

An optimal trajectory planning problem for a single-link, flexible joint manipulator is studied. A global feedback-linearization is first applied to formulate the nonlinear inequality-constrained optimization problem in a suitable way. Then, an exact and explicit structural formula for the optimal solution of the problem is derived and the solution is shown to be unique. It turns out that the optimal trajectory planning and control can be done off-line, so that the proposed method is applicable to both theoretical analysis and real time tele-robotics control engineering.

Constrained trajectory optimization for kinematically redundant arms

NASA Technical Reports Server (NTRS)

Carignan, Craig R.; Tarrant, Janice M.

1990-01-01

Two velocity optimization schemes for resolving redundant joint configurations are compared. The Extended Moore-Penrose Technique minimizes the joint velocities and avoids obstacles indirectly by adjoining a cost gradient to the solution. A new method can incorporate inequality constraints directly to avoid obstacles and singularities in the workspace. A four-link arm example is used to illustrate singularity avoidance while tracking desired end-effector paths.

A majorized Newton-CG augmented Lagrangian-based finite element method for 3D restoration of geological models

NASA Astrophysics Data System (ADS)

Tang, Peipei; Wang, Chengjing; Dai, Xiaoxia

2016-04-01

In this paper, we propose a majorized Newton-CG augmented Lagrangian-based finite element method for 3D elastic frictionless contact problems. In this scheme, we discretize the restoration problem via the finite element method and reformulate it to a constrained optimization problem. Then we apply the majorized Newton-CG augmented Lagrangian method to solve the optimization problem, which is very suitable for the ill-conditioned case. Numerical results demonstrate that the proposed method is a very efficient algorithm for various large-scale 3D restorations of geological models, especially for the restoration of geological models with complicated faults.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Effective Teaching of Economics: A Constrained Optimization Problem?

ERIC Educational Resources Information Center

Hultberg, Patrik T.; Calonge, David Santandreu

2017-01-01

One of the fundamental tenets of economics is that decisions are often the result of optimization problems subject to resource constraints. Consumers optimize utility, subject to constraints imposed by prices and income. As economics faculty, instructors attempt to maximize student learning while being constrained by their own and students'…

Design of optimally normal minimum gain controllers by continuation method

NASA Technical Reports Server (NTRS)

Lim, K. B.; Juang, J.-N.; Kim, Z. C.

1989-01-01

A measure of the departure from normality is investigated for system robustness. An attractive feature of the normality index is its simplicity for pole placement designs. To allow a tradeoff between system robustness and control effort, a cost function consisting of the sum of a norm of weighted gain matrix and a normality index is minimized. First- and second-order necessary conditions for the constrained optimization problem are derived and solved by a Newton-Raphson algorithm imbedded into a one-parameter family of neighboring zero problems. The method presented allows the direct computation of optimal gains in terms of robustness and control effort for pole placement problems.

A comparison of optimization algorithms for localized in vivo B0 shimming.

PubMed

Nassirpour, Sahar; Chang, Paul; Fillmer, Ariane; Henning, Anke

2018-02-01

To compare several different optimization algorithms currently used for localized in vivo B 0 shimming, and to introduce a novel, fast, and robust constrained regularized algorithm (ConsTru) for this purpose. Ten different optimization algorithms (including samples from both generic and dedicated least-squares solvers, and a novel constrained regularized inversion method) were implemented and compared for shimming in five different shimming volumes on 66 in vivo data sets from both 7 T and 9.4 T. The best algorithm was chosen to perform single-voxel spectroscopy at 9.4 T in the frontal cortex of the brain on 10 volunteers. The results of the performance tests proved that the shimming algorithm is prone to unstable solutions if it depends on the value of a starting point, and is not regularized to handle ill-conditioned problems. The ConsTru algorithm proved to be the most robust, fast, and efficient algorithm among all of the chosen algorithms. It enabled acquisition of spectra of reproducible high quality in the frontal cortex at 9.4 T. For localized in vivo B 0 shimming, the use of a dedicated linear least-squares solver instead of a generic nonlinear one is highly recommended. Among all of the linear solvers, the constrained regularized method (ConsTru) was found to be both fast and most robust. Magn Reson Med 79:1145-1156, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Nonlinear programming extensions to rational function approximation methods for unsteady aerodynamic forces

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1988-01-01

The approximation of unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft are discussed. Two methods of formulating these approximations are extended to include the same flexibility in constraining the approximations and the same methodology in optimizing nonlinear parameters as another currently used extended least-squares method. Optimal selection of nonlinear parameters is made in each of the three methods by use of the same nonlinear, nongradient optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is lower order than that required when no optimization of the nonlinear terms is performed. The free linear parameters are determined using the least-squares matrix techniques of a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from different approaches are described and results are presented that show comparative evaluations from application of each of the extended methods to a numerical example.

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Eleshaky, Mohamed E.

1991-01-01

A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.

Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.

1997-01-01

Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.

Interconnection-wide hour-ahead scheduling in the presence of intermittent renewables and demand response: A surplus maximizing approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Behboodi, Sahand; Chassin, David P.; Djilali, Ned

This study describes a new approach for solving the multi-area electricity resource allocation problem when considering both intermittent renewables and demand response. The method determines the hourly inter-area export/import set that maximizes the interconnection (global) surplus satisfying transmission, generation and load constraints. The optimal inter-area transfer set effectively makes the electricity price uniform over the interconnection apart from constrained areas, which overall increases the consumer surplus more than it decreases the producer surplus. The method is computationally efficient and suitable for use in simulations that depend on optimal scheduling models. The method is demonstrated on a system that represents Northmore » America Western Interconnection for the planning year of 2024. Simulation results indicate that effective use of interties reduces the system operation cost substantially. Excluding demand response, both the unconstrained and the constrained scheduling solutions decrease the global production cost (and equivalently increase the global economic surplus) by 12.30B and 10.67B per year, respectively, when compared to the standalone case in which each control area relies only on its local supply resources. This cost saving is equal to 25% and 22% of the annual production cost. Including 5% demand response, the constrained solution decreases the annual production cost by 10.70B, while increases the annual surplus by 9.32B in comparison to the standalone case.« less

Interconnection-wide hour-ahead scheduling in the presence of intermittent renewables and demand response: A surplus maximizing approach

DOE PAGES

Behboodi, Sahand; Chassin, David P.; Djilali, Ned; ...

2016-12-23

This study describes a new approach for solving the multi-area electricity resource allocation problem when considering both intermittent renewables and demand response. The method determines the hourly inter-area export/import set that maximizes the interconnection (global) surplus satisfying transmission, generation and load constraints. The optimal inter-area transfer set effectively makes the electricity price uniform over the interconnection apart from constrained areas, which overall increases the consumer surplus more than it decreases the producer surplus. The method is computationally efficient and suitable for use in simulations that depend on optimal scheduling models. The method is demonstrated on a system that represents Northmore » America Western Interconnection for the planning year of 2024. Simulation results indicate that effective use of interties reduces the system operation cost substantially. Excluding demand response, both the unconstrained and the constrained scheduling solutions decrease the global production cost (and equivalently increase the global economic surplus) by 12.30B and 10.67B per year, respectively, when compared to the standalone case in which each control area relies only on its local supply resources. This cost saving is equal to 25% and 22% of the annual production cost. Including 5% demand response, the constrained solution decreases the annual production cost by 10.70B, while increases the annual surplus by 9.32B in comparison to the standalone case.« less

Constrained Multiobjective Biogeography Optimization Algorithm

PubMed Central

Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

2014-01-01

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591

Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

He, Hao; Wang, Jun; Zhu, Jiang; Li, Shaoqian

2010-12-01

In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP), which can be solved by standard linear programming (LP) method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

Global optimization framework for solar building design

NASA Astrophysics Data System (ADS)

Silva, N.; Alves, N.; Pascoal-Faria, P.

2017-07-01

The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal

ERIC Educational Resources Information Center

Steinley, Douglas; Hubert, Lawrence

2008-01-01

This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…

Relative frequencies of constrained events in stochastic processes: An analytical approach.

PubMed

Rusconi, S; Akhmatskaya, E; Sokolovski, D; Ballard, N; de la Cal, J C

2015-10-01

The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They relies on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specify in advance, in many real life applications. Knowing the shapes of PDFs, and using experimental data, different optimization schemes can be applied in order to evaluate probability density functions and, therefore, the properties of the studied system. Such methods, however, are computationally demanding, and often not feasible. We show that, in the case where experimentally accessed properties are directly related to the frequencies of events involved, it may be possible to replace the heavy Monte Carlo core of optimization schemes with an analytical solution. Such a replacement not only provides a more accurate estimation of the properties of the process, but also reduces the simulation time by a factor of order of the sample size (at least ≈10(4)). The proposed analytical approach is valid for any choice of PDF. The accuracy, computational efficiency, and advantages of the method over MC procedures are demonstrated in the exactly solvable case and in the evaluation of branching fractions in controlled radical polymerization (CRP) of acrylic monomers. This polymerization can be modeled by a constrained stochastic process. Constrained systems are quite common, and this makes the method useful for various applications.

Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method.

PubMed

Lan, Yihua; Li, Cunhua; Ren, Haozheng; Zhang, Yong; Min, Zhifang

2012-10-21

A new heuristic algorithm based on the so-called geometric distance sorting technique is proposed for solving the fluence map optimization with dose-volume constraints which is one of the most essential tasks for inverse planning in IMRT. The framework of the proposed method is basically an iterative process which begins with a simple linear constrained quadratic optimization model without considering any dose-volume constraints, and then the dose constraints for the voxels violating the dose-volume constraints are gradually added into the quadratic optimization model step by step until all the dose-volume constraints are satisfied. In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming. For choosing the proper candidate voxels for the current dose constraint adding, a so-called geometric distance defined in the transformed standard quadratic form of the fluence map optimization model was used to guide the selection of the voxels. The new geometric distance sorting technique can mostly reduce the unexpected increase of the objective function value caused inevitably by the constraint adding. It can be regarded as an upgrading to the traditional dose sorting technique. The geometry explanation for the proposed method is also given and a proposition is proved to support our heuristic idea. In addition, a smart constraint adding/deleting strategy is designed to ensure a stable iteration convergence. The new algorithm is tested on four cases including head-neck, a prostate, a lung and an oropharyngeal, and compared with the algorithm based on the traditional dose sorting technique. Experimental results showed that the proposed method is more suitable for guiding the selection of new constraints than the traditional dose sorting method, especially for the cases whose target regions are in non-convex shapes. It is a more efficient optimization technique to some extent for choosing constraints than the dose sorting method. By integrating a smart constraint adding/deleting scheme within the iteration framework, the new technique builds up an improved algorithm for solving the fluence map optimization with dose-volume constraints.

A Model for Engaging Students in a Research Experience Involving Variational Techniques, Mathematica, and Descent Methods.

ERIC Educational Resources Information Center

Mahavier, W. Ted

2002-01-01

Describes a two-semester numerical methods course that serves as a research experience for undergraduate students without requiring external funding or the modification of current curriculum. Uses an engineering problem to introduce students to constrained optimization via a variation of the traditional isoperimetric problem of finding the curve…

ADS: A FORTRAN program for automated design synthesis: Version 1.10

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1985-01-01

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis - Version 1.10) is a FORTRAN program for solution of nonlinear constrained optimization problems. The program is segmented into three levels: strategy, optimizer, and one-dimensional search. At each level, several options are available so that a total of over 100 possible combinations can be created. Examples of available strategies are sequential unconstrained minimization, the Augmented Lagrange Multiplier method, and Sequential Linear Programming. Available optimizers include variable metric methods and the Method of Feasible Directions as examples, and one-dimensional search options include polynomial interpolation and the Golden Section method as examples. Emphasis is placed on ease of use of the program. All information is transferred via a single parameter list. Default values are provided for all internal program parameters such as convergence criteria, and the user is given a simple means to over-ride these, if desired.

Optimal control penalty finite elements - Applications to integrodifferential equations

NASA Astrophysics Data System (ADS)

Chung, T. J.

The application of the optimal-control/penalty finite-element method to the solution of integrodifferential equations in radiative-heat-transfer problems (Chung et al.; Chung and Kim, 1982) is discussed and illustrated. The nonself-adjointness of the convective terms in the governing equations is treated by utilizing optimal-control cost functions and employing penalty functions to constrain auxiliary equations which permit the reduction of second-order derivatives to first order. The OCPFE method is applied to combined-mode heat transfer by conduction, convection, and radiation, both without and with scattering and viscous dissipation; the results are presented graphically and compared to those obtained by other methods. The OCPFE method is shown to give good results in cases where standard Galerkin FE fail, and to facilitate the investigation of scattering and dissipation effects.

Resource Constrained Planning of Multiple Projects with Separable Activities

NASA Astrophysics Data System (ADS)

Fujii, Susumu; Morita, Hiroshi; Kanawa, Takuya

In this study we consider a resource constrained planning problem of multiple projects with separable activities. This problem provides a plan to process the activities considering a resource availability with time window. We propose a solution algorithm based on the branch and bound method to obtain the optimal solution minimizing the completion time of all projects. We develop three methods for improvement of computational efficiency, that is, to obtain initial solution with minimum slack time rule, to estimate lower bound considering both time and resource constraints and to introduce an equivalence relation for bounding operation. The effectiveness of the proposed methods is demonstrated by numerical examples. Especially as the number of planning projects increases, the average computational time and the number of searched nodes are reduced.

Helicopter Control Energy Reduction Using Moving Horizontal Tail

PubMed Central

Oktay, Tugrul; Sal, Firat

2015-01-01

Helicopter moving horizontal tail (i.e., MHT) strategy is applied in order to save helicopter flight control system (i.e., FCS) energy. For this intention complex, physics-based, control-oriented nonlinear helicopter models are used. Equations of MHT are integrated into these models and they are together linearized around straight level flight condition. A specific variance constrained control strategy, namely, output variance constrained Control (i.e., OVC) is utilized for helicopter FCS. Control energy savings due to this MHT idea with respect to a conventional helicopter are calculated. Parameters of helicopter FCS and dimensions of MHT are simultaneously optimized using a stochastic optimization method, namely, simultaneous perturbation stochastic approximation (i.e., SPSA). In order to observe improvement in behaviors of classical controls closed loop analyses are done. PMID:26180841

Optimal mistuning for enhanced aeroelastic stability of transonic fans

NASA Technical Reports Server (NTRS)

Hall, K. C.; Crawley, E. F.

1983-01-01

An inverse design procedure was developed for the design of a mistuned rotor. The design requirements are that the stability margin of the eigenvalues of the aeroelastic system be greater than or equal to some minimum stability margin, and that the mass added to each blade be positive. The objective was to achieve these requirements with a minimal amount of mistuning. Hence, the problem was posed as a constrained optimization problem. The constrained minimization problem was solved by the technique of mathematical programming via augmented Lagrangians. The unconstrained minimization phase of this technique was solved by the variable metric method. The bladed disk was modelled as being composed of a rigid disk mounted on a rigid shaft. Each of the blades were modelled with a single tosional degree of freedom.

Experimental and simulation studies of multivariable adaptive optimization of continuous bioreactors using bilevel forgetting factors.

PubMed

Chang, Y K; Lim, H C

1989-08-20

A multivariable on-line adaptive optimization algorithm using a bilevel forgetting factor method was developed and applied to a continuous baker's yeast culture in simulation and experimental studies to maximize the cellular productivity by manipulating the dilution rate and the temperature. The algorithm showed a good optimization speed and a good adaptability and reoptimization capability. The algorithm was able to stably maintain the process around the optimum point for an extended period of time. Two cases were investigated: an unconstrained and a constrained optimization. In the constrained optimization the ethanol concentration was used as an index for the baking quality of yeast cells. An equality constraint with a quadratic penalty was imposed on the ethanol concentration to keep its level close to a hypothetical "optimum" value. The developed algorithm was experimentally applied to a baker's yeast culture to demonstrate its validity. Only unconstrained optimization was carried out experimentally. A set of tuning parameter values was suggested after evaluating the results from several experimental runs. With those tuning parameter values the optimization took 50-90 h. At the attained steady state the dilution rate was 0.310 h(-1) the temperature 32.8 degrees C, and the cellular productivity 1.50 g/L/h.

Optimizing Department of Defense Acquisition Development Test and Evaluation Scheduling

DTIC Science & Technology

2015-06-01

CPM Critical Path Method DOD Department of Defense DT&E development test and evaluation EMD engineering and manufacturing development GAMS...these, including the Program Evaluation Review Technique (PERT), the Critical Path Method ( CPM ), and the resource- constrained project-scheduling...problem (RCPSP). These are of particular interest to this thesis as the current scheduling method uses elements of the PERT/ CPM , and the test

Correction method for stripe nonuniformity.

PubMed

Qian, Weixian; Chen, Qian; Gu, Guohua; Guan, Zhiqiang

2010-04-01

Stripe nonuniformity is very typical in line infrared focal plane arrays (IR-FPA) and uncooled staring IR-FPA. In this paper, the mechanism of the stripe nonuniformity is analyzed, and the gray-scale co-occurrence matrix theory and optimization theory are studied. Through these efforts, the stripe nonuniformity correction problem is translated into the optimization problem. The goal of the optimization is to find the minimal energy of the image's line gradient. After solving the constrained nonlinear optimization equation, the parameters of the stripe nonuniformity correction are obtained and the stripe nonuniformity correction is achieved. The experiments indicate that this algorithm is effective and efficient.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

NASA Astrophysics Data System (ADS)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

The use of optimization techniques to design controlled diffusion compressor blading

NASA Technical Reports Server (NTRS)

Sanger, N. L.

1982-01-01

A method for automating compressor blade design using numerical optimization, and applied to the design of a controlled diffusion stator blade row is presented. A general purpose optimization procedure is employed, based on conjugate directions for locally unconstrained problems and on feasible directions for locally constrained problems. Coupled to the optimizer is an analysis package consisting of three analysis programs which calculate blade geometry, inviscid flow, and blade surface boundary layers. The optimizing concepts and selection of design objective and constraints are described. The procedure for automating the design of a two dimensional blade section is discussed, and design results are presented.

Constrained growth flips the direction of optimal phenological responses among annual plants.

PubMed

Lindh, Magnus; Johansson, Jacob; Bolmgren, Kjell; Lundström, Niklas L P; Brännström, Åke; Jonzén, Niclas

2016-03-01

Phenological changes among plants due to climate change are well documented, but often hard to interpret. In order to assess the adaptive value of observed changes, we study how annual plants with and without growth constraints should optimize their flowering time when productivity and season length changes. We consider growth constraints that depend on the plant's vegetative mass: self-shading, costs for nonphotosynthetic structural tissue and sibling competition. We derive the optimal flowering time from a dynamic energy allocation model using optimal control theory. We prove that an immediate switch (bang-bang control) from vegetative to reproductive growth is optimal with constrained growth and constant mortality. Increasing mean productivity, while keeping season length constant and growth unconstrained, delayed the optimal flowering time. When growth was constrained and productivity was relatively high, the optimal flowering time advanced instead. When the growth season was extended equally at both ends, the optimal flowering time was advanced under constrained growth and delayed under unconstrained growth. Our results suggests that growth constraints are key factors to consider when interpreting phenological flowering responses. It can help to explain phenological patterns along productivity gradients, and links empirical observations made on calendar scales with life-history theory. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Multiple-Vehicle Resource-Constrained Navigation in the Deep Ocean

DTIC Science & Technology

2011-09-01

Kalman filtering is often used in practice with intermit - tent observations, which uses the simple intuitive result that the optimal method for...trajectory? This level is concerned with fast vehicle dynamics, which are highly dependent on the particular vehicle design and hydrodynamics. Some

Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

NASA Astrophysics Data System (ADS)

Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

2017-10-01

Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

Optimal dynamic control of invasions: applying a systematic conservation approach.

PubMed

Adams, Vanessa M; Setterfield, Samantha A

2015-06-01

The social, economic, and environmental impacts of invasive plants are well recognized. However, these variable impacts are rarely accounted for in the spatial prioritization of funding for weed management. We examine how current spatially explicit prioritization methods can be extended to identify optimal budget allocations to both eradication and control measures of invasive species to minimize the costs and likelihood of invasion. Our framework extends recent approaches to systematic prioritization of weed management to account for multiple values that are threatened by weed invasions with a multi-year dynamic prioritization approach. We apply our method to the northern portion of the Daly catchment in the Northern Territory, which has significant conservation values that are threatened by gamba grass (Andropogon gayanus), a highly invasive species recognized by the Australian government as a Weed of National Significance (WONS). We interface Marxan, a widely applied conservation planning tool, with a dynamic biophysical model of gamba grass to optimally allocate funds to eradication and control programs under two budget scenarios comparing maximizing gain (MaxGain) and minimizing loss (MinLoss) optimization approaches. The prioritizations support previous findings that a MinLoss approach is a better strategy when threats are more spatially variable than conservation values. Over a 10-year simulation period, we find that a MinLoss approach reduces future infestations by ~8% compared to MaxGain in the constrained budget scenarios and ~12% in the unlimited budget scenarios. We find that due to the extensive current invasion and rapid rate of spread, allocating the annual budget to control efforts is more efficient than funding eradication efforts when there is a constrained budget. Under a constrained budget, applying the most efficient optimization scenario (control, minloss) reduces spread by ~27% compared to no control. Conversely, if the budget is unlimited it is more efficient to fund eradication efforts and reduces spread by ~65% compared to no control.

Local structure of equality constrained NLP problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mari, J.

We show that locally around a feasible point, the behavior of an equality constrained nonlinear program is described by the gradient and the Hessian of the Lagrangian on the tangent subspace. In particular this holds true for reduced gradient approaches. Applying the same ideas to the control of nonlinear ODE:s, one can device first and second order methods that can be applied also to stiff problems. We finally describe an application of these ideas to the optimization of the production of human growth factor by fed-batch fermentation.

A linearized theory method of constrained optimization for supersonic cruise wing design

NASA Technical Reports Server (NTRS)

Miller, D. S.; Carlson, H. W.; Middleton, W. D.

1976-01-01

A linearized theory wing design and optimization procedure which allows physical realism and practical considerations to be imposed as constraints on the optimum (least drag due to lift) solution is discussed and examples of application are presented. In addition to the usual constraints on lift and pitching moment, constraints are imposed on wing surface ordinates and wing upper surface pressure levels and gradients. The design procedure also provides the capability of including directly in the optimization process the effects of other aircraft components such as a fuselage, canards, and nacelles.

Absolute Stability Analysis of a Phase Plane Controlled Spacecraft

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; Plummer, Michael; Bedrossian, Nazareth; Hall, Charles; Jackson, Mark; Spanos, Pol

2010-01-01

Many aerospace attitude control systems utilize phase plane control schemes that include nonlinear elements such as dead zone and ideal relay. To evaluate phase plane control robustness, stability margin prediction methods must be developed. Absolute stability is extended to predict stability margins and to define an abort condition. A constrained optimization approach is also used to design flex filters for roll control. The design goal is to optimize vehicle tracking performance while maintaining adequate stability margins. Absolute stability is shown to provide satisfactory stability constraints for the optimization.

COPS: Large-scale nonlinearly constrained optimization problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bondarenko, A.S.; Bortz, D.M.; More, J.J.

2000-02-10

The authors have started the development of COPS, a collection of large-scale nonlinearly Constrained Optimization Problems. The primary purpose of this collection is to provide difficult test cases for optimization software. Problems in the current version of the collection come from fluid dynamics, population dynamics, optimal design, and optimal control. For each problem they provide a short description of the problem, notes on the formulation of the problem, and results of computational experiments with general optimization solvers. They currently have results for DONLP2, LANCELOT, MINOS, SNOPT, and LOQO.

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 2: Analytic manual

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Space Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows subproblems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

An open-source model and solution method to predict co-contraction in the finger.

PubMed

MacIntosh, Alexander R; Keir, Peter J

2017-10-01

A novel open-source biomechanical model of the index finger with an electromyography (EMG)-constrained static optimization solution method are developed with the goal of improving co-contraction estimates and providing means to assess tendon tension distribution through the finger. The Intrinsic model has four degrees of freedom and seven muscles (with a 14 component extensor mechanism). A novel plugin developed for the OpenSim modelling software applied the EMG-constrained static optimization solution method. Ten participants performed static pressing in three finger postures and five dynamic free motion tasks. Index finger 3D kinematics, force (5, 15, 30 N), and EMG (4 extrinsic muscles and first dorsal interosseous) were used in the analysis. The Intrinsic model predicted co-contraction increased by 29% during static pressing over the existing model. Further, tendon tension distribution patterns and forces, known to be essential to produce finger action, were determined by the model across all postures. The Intrinsic model and custom solution method improved co-contraction estimates to facilitate force propagation through the finger. These tools improve our interpretation of loads in the finger to develop better rehabilitation and workplace injury risk reduction strategies.

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

PubMed Central

Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito

2016-01-01

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474

Generalized Pattern Search methods for a class of nonsmooth optimization problems with structure

NASA Astrophysics Data System (ADS)

Bogani, C.; Gasparo, M. G.; Papini, A.

2009-07-01

We propose a Generalized Pattern Search (GPS) method to solve a class of nonsmooth minimization problems, where the set of nondifferentiability is included in the union of known hyperplanes and, therefore, is highly structured. Both unconstrained and linearly constrained problems are considered. At each iteration the set of poll directions is enforced to conform to the geometry of both the nondifferentiability set and the boundary of the feasible region, near the current iterate. This is the key issue to guarantee the convergence of certain subsequences of iterates to points which satisfy first-order optimality conditions. Numerical experiments on some classical problems validate the method.

Optimization of flexible wing structures subject to strength and induced drag constraints

NASA Technical Reports Server (NTRS)

Haftka, R. T.

1977-01-01

An optimization procedure for designing wing structures subject to stress, strain, and drag constraints is presented. The optimization method utilizes an extended penalty function formulation for converting the constrained problem into a series of unconstrained ones. Newton's method is used to solve the unconstrained problems. An iterative analysis procedure is used to obtain the displacements of the wing structure including the effects of load redistribution due to the flexibility of the structure. The induced drag is calculated from the lift distribution. Approximate expressions for the constraints used during major portions of the optimization process enhance the efficiency of the procedure. A typical fighter wing is used to demonstrate the procedure. Aluminum and composite material designs are obtained. The tradeoff between weight savings and drag reduction is investigated.

Level-Set Topology Optimization with Aeroelastic Constraints

NASA Technical Reports Server (NTRS)

Dunning, Peter D.; Stanford, Bret K.; Kim, H. Alicia

2015-01-01

Level-set topology optimization is used to design a wing considering skin buckling under static aeroelastic trim loading, as well as dynamic aeroelastic stability (flutter). The level-set function is defined over the entire 3D volume of a transport aircraft wing box. Therefore, the approach is not limited by any predefined structure and can explore novel configurations. The Sequential Linear Programming (SLP) level-set method is used to solve the constrained optimization problems. The proposed method is demonstrated using three problems with mass, linear buckling and flutter objective and/or constraints. A constraint aggregation method is used to handle multiple buckling constraints in the wing skins. A continuous flutter constraint formulation is used to handle difficulties arising from discontinuities in the design space caused by a switching of the critical flutter mode.

A trust region-based approach to optimize triple response systems

NASA Astrophysics Data System (ADS)

Fan, Shu-Kai S.; Fan, Chihhao; Huang, Chia-Fen

2014-05-01

This article presents a new computing procedure for the global optimization of the triple response system (TRS) where the response functions are non-convex quadratics and the input factors satisfy a radial constrained region of interest. The TRS arising from response surface modelling can be approximated using a nonlinear mathematical program that considers one primary objective function and two secondary constraint functions. An optimization algorithm named the triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the non-degenerate TRS. In TRSALG, the Lagrange multipliers of the secondary functions are determined using the Hooke-Jeeves search method and the Lagrange multiplier of the radial constraint is located using the trust region method within the global optimality space. The proposed algorithm is illustrated in terms of three examples appearing in the quality-control literature. The results of TRSALG compared to a gradient-based method are also presented.

Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.

1990-01-01

Practical engineering application can often be formulated in the form of a constrained optimization problem. There are several solution algorithms for solving a constrained optimization problem. One approach is to convert a constrained problem into a series of unconstrained problems. Furthermore, unconstrained solution algorithms can be used as part of the constrained solution algorithms. Structural optimization is an iterative process where one starts with an initial design, a finite element structure analysis is then performed to calculate the response of the system (such as displacements, stresses, eigenvalues, etc.). Based upon the sensitivity information on the objective and constraint functions, an optimizer such as ADS or IDESIGN, can be used to find the new, improved design. For the structural analysis phase, the equation solver for the system of simultaneous, linear equations plays a key role since it is needed for either static, or eigenvalue, or dynamic analysis. For practical, large-scale structural analysis-synthesis applications, computational time can be excessively large. Thus, it is necessary to have a new structural analysis-synthesis code which employs new solution algorithms to exploit both parallel and vector capabilities offered by modern, high performance computers such as the Convex, Cray-2 and Cray-YMP computers. The objective of this research project is, therefore, to incorporate the latest development in the parallel-vector equation solver, PVSOLVE into the widely popular finite-element production code, such as the SAP-4. Furthermore, several nonlinear unconstrained optimization subroutines have also been developed and tested under a parallel computer environment. The unconstrained optimization subroutines are not only useful in their own right, but they can also be incorporated into a more popular constrained optimization code, such as ADS.

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Modern Optimization Methods in Minimum Weight Design of Elastic Annular Rotating Disk with Variable Thickness

NASA Astrophysics Data System (ADS)

Jafari, S.; Hojjati, M. H.

2011-12-01

Rotating disks work mostly at high angular velocity and this results a large centrifugal force and consequently induce large stresses and deformations. Minimizing weight of such disks yields to benefits such as low dead weights and lower costs. This paper aims at finding an optimal disk thickness profile for minimum weight design using the simulated annealing (SA) and particle swarm optimization (PSO) as two modern optimization techniques. In using semi-analytical the radial domain of the disk is divided into some virtual sub-domains as rings where the weight of each rings must be minimized. Inequality constrain equation used in optimization is to make sure that maximum von Mises stress is always less than yielding strength of the material of the disk and rotating disk does not fail. The results show that the minimum weight obtained for all two methods is almost identical. The PSO method gives a profile with slightly less weight (6.9% less than SA) while the implementation of both PSO and SA methods are easy and provide more flexibility compared with classical methods.

Design optimization and probabilistic analysis of a hydrodynamic journal bearing

NASA Technical Reports Server (NTRS)

Liniecki, Alexander G.

1990-01-01

A nonlinear constrained optimization of a hydrodynamic bearing was performed yielding three main variables: radial clearance, bearing length to diameter ratio, and lubricating oil viscosity. As an objective function a combined model of temperature rise and oil supply has been adopted. The optimized model of the bearing has been simulated for population of 1000 cases using Monte Carlo statistical method. It appeared that the so called 'optimal solution' generated more than 50 percent of failed bearings, because their minimum oil film thickness violated stipulated minimum constraint value. As a remedy change of oil viscosity is suggested after several sensitivities of variables have been investigated.

Modelling night-time ecosystem respiration by a constrained source optimization method

Treesearch

Chun-Tai Lai; Gabriel Katul; John Butnor; David Ellsworth; Ram Oren

2002-01-01

One of the main challenges to quantifying ecosystem carbon budgets is properly quantifying the magnitude of night-time ecosystem respiration. Inverse Lagrangian dispersion analysis provides a promising approach to addressing such a problem when measured mean CO2 concentration profiles and nocturnal velocity statistics are available. An inverse...

Investigation on the reproduction performance versus acoustic contrast control in sound field synthesis.

PubMed

Bai, Mingsian R; Wen, Jheng-Ciang; Hsu, Hoshen; Hua, Yi-Hsin; Hsieh, Yu-Hao

2014-10-01

A sound reconstruction system is proposed for audio reproduction with extended sweet spot and reduced reflections. An equivalent source method (ESM)-based sound field synthesis (SFS) approach, with the aid of dark zone minimization is adopted in the study. Conventional SFS that is based on the free-field assumption suffers from synthesis error due to boundary reflections. To tackle the problem, the proposed system utilizes convex optimization in designing array filters with both reproduction performance and acoustic contrast taken into consideration. Control points are deployed in the dark zone to minimize the reflections from the walls. Two approaches are employed to constrain the pressure and velocity in the dark zone. Pressure matching error (PME) and acoustic contrast (AC) are used as performance measures in simulations and experiments for a rectangular loudspeaker array. Perceptual Evaluation of Audio Quality (PEAQ) is also used to assess the audio reproduction quality. The results show that the pressure-constrained (PC) method yields better acoustic contrast, but poorer reproduction performance than the pressure-velocity constrained (PVC) method. A subjective listening test also indicates that the PVC method is the preferred method in a live room.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

PubMed Central

Hu, Yu-Chen

2018-01-01

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%. PMID:29702607

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

IPOST is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence fo trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the coat function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

A general optimality criteria algorithm for a class of engineering optimization problems

NASA Astrophysics Data System (ADS)

Belegundu, Ashok D.

2015-05-01

An optimality criteria (OC)-based algorithm for optimization of a general class of nonlinear programming (NLP) problems is presented. The algorithm is only applicable to problems where the objective and constraint functions satisfy certain monotonicity properties. For multiply constrained problems which satisfy these assumptions, the algorithm is attractive compared with existing NLP methods as well as prevalent OC methods, as the latter involve computationally expensive active set and step-size control strategies. The fixed point algorithm presented here is applicable not only to structural optimization problems but also to certain problems as occur in resource allocation and inventory models. Convergence aspects are discussed. The fixed point update or resizing formula is given physical significance, which brings out a strength and trim feature. The number of function evaluations remains independent of the number of variables, allowing the efficient solution of problems with large number of variables.

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

NASA Astrophysics Data System (ADS)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis shows that terrestrial carbon and water cycle simulations in monsoon Asia were greatly improved, and the use of multiple satellite observations with this framework is an effective way for improving terrestrial biosphere models.

Constrained Multi-Level Algorithm for Trajectory Optimization

NASA Astrophysics Data System (ADS)

Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi

The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in diagonalised methods, being the only single update with quadratic convergence. For a single level, the diagonalised multiplier method (DMM) is described in Ref.5. The main advantage of the two-level analogue of the DMM approach is that it avoids the inner loop optimizations required in the other methods. The scheme also introduces a gradient change measure to reduce the computational time needed to calculate the gradients. It is demonstrated that the new multi-level scheme leads to a robust procedure to handle the sensitivity of the constraints, and the multiple objectives of different trajectory phases. Ref. 1. Fahroo, F and Ross, M., " A Spectral Patching Method for Direct Trajectory Optimization" The Journal of the Astronautical Sciences, Vol.48, 2000, pp.269-286 Ref. 2. Phililps, C.A. and Drake, J.C., "Trajectory Optimization for a Missile using a Multitier Approach" Journal of Spacecraft and Rockets, Vol.37, 2000, pp.663-669 Ref. 3. Gath, P.F., and Calise, A.J., " Optimization of Launch Vehicle Ascent Trajectories with Path Constraints and Coast Arcs", Journal of Guidance, Control, and Dynamics, Vol. 24, 2001, pp.296-304 Ref. 4. Betts, J.T., " Survey of Numerical Methods for Trajectory Optimization", Journal of Guidance, Control, and Dynamics, Vol.21, 1998, pp. 193-207 Ref. 5. Adimurthy, V., " Launch Vehicle Trajectory Optimization", Acta Astronautica, Vol.15, 1987, pp.845-850.

The reduced space Sequential Quadratic Programming (SQP) method for calculating the worst resonance response of nonlinear systems

NASA Astrophysics Data System (ADS)

Liao, Haitao; Wu, Wenwang; Fang, Daining

2018-07-01

A coupled approach combining the reduced space Sequential Quadratic Programming (SQP) method with the harmonic balance condensation technique for finding the worst resonance response is developed. The nonlinear equality constraints of the optimization problem are imposed on the condensed harmonic balance equations. Making use of the null space decomposition technique, the original optimization formulation in the full space is mathematically simplified, and solved in the reduced space by means of the reduced SQP method. The transformation matrix that maps the full space to the null space of the constrained optimization problem is constructed via the coordinate basis scheme. The removal of the nonlinear equality constraints is accomplished, resulting in a simple optimization problem subject to bound constraints. Moreover, second order correction technique is introduced to overcome Maratos effect. The combination application of the reduced SQP method and condensation technique permits a large reduction of the computational cost. Finally, the effectiveness and applicability of the proposed methodology is demonstrated by two numerical examples.

SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weir, V; Zhang, J

Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. Themore » phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Simonetto, Andrea

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are establishedmore » to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.« less

Fuzzy robust credibility-constrained programming for environmental management and planning.

PubMed

Zhang, Yimei; Hang, Guohe

2010-06-01

In this study, a fuzzy robust credibility-constrained programming (FRCCP) is developed and applied to the planning for waste management systems. It incorporates the concepts of credibility-based chance-constrained programming and robust programming within an optimization framework. The developed method can reflect uncertainties presented as possibility-density by fuzzy-membership functions. Fuzzy credibility constraints are transformed to the crisp equivalents with different credibility levels, and ordinary fuzzy inclusion constraints are determined by their robust deterministic constraints by setting a-cut levels. The FRCCP method can provide different system costs under different credibility levels (lambda). From the results of sensitivity analyses, the operation cost of the landfill is a critical parameter. For the management, any factors that would induce cost fluctuation during landfilling operation would deserve serious observation and analysis. By FRCCP, useful solutions can be obtained to provide decision-making support for long-term planning of solid waste management systems. It could be further enhanced through incorporating methods of inexact analysis into its framework. It can also be applied to other environmental management problems.

Multi-point objective-oriented sequential sampling strategy for constrained robust design

NASA Astrophysics Data System (ADS)

Zhu, Ping; Zhang, Siliang; Chen, Wei

2015-03-01

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

PubMed

Wu, Sheng; Jin, Qibing; Zhang, Ridong; Zhang, Junfeng; Gao, Furong

2017-07-01

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Constrained Burn Optimization for the International Space Station

NASA Technical Reports Server (NTRS)

Brown, Aaron J.; Jones, Brandon A.

2017-01-01

In long-term trajectory planning for the International Space Station (ISS), translational burns are currently targeted sequentially to meet the immediate trajectory constraints, rather than simultaneously to meet all constraints, do not employ gradient-based search techniques, and are not optimized for a minimum total deltav (v) solution. An analytic formulation of the constraint gradients is developed and used in an optimization solver to overcome these obstacles. Two trajectory examples are explored, highlighting the advantage of the proposed method over the current approach, as well as the potential v and propellant savings in the event of propellant shortages.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Poster — Thur Eve — 69: Computational Study of DVH-guided Cancer Treatment Planning Optimization Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghomi, Pooyan Shirvani; Zinchenko, Yuriy

2014-08-15

Purpose: To compare methods to incorporate the Dose Volume Histogram (DVH) curves into the treatment planning optimization. Method: The performance of three methods, namely, the conventional Mixed Integer Programming (MIP) model, a convex moment-based constrained optimization approach, and an unconstrained convex moment-based penalty approach, is compared using anonymized data of a prostate cancer patient. Three plans we generated using the corresponding optimization models. Four Organs at Risk (OARs) and one Tumor were involved in the treatment planning. The OARs and Tumor were discretized into total of 50,221 voxels. The number of beamlets was 943. We used commercially available optimization softwaremore » Gurobi and Matlab to solve the models. Plan comparison was done by recording the model runtime followed by visual inspection of the resulting dose volume histograms. Conclusion: We demonstrate the effectiveness of the moment-based approaches to replicate the set of prescribed DVH curves. The unconstrained convex moment-based penalty approach is concluded to have the greatest potential to reduce the computational effort and holds a promise of substantial computational speed up.« less

Water Budget Estimation by Assimilating Multiple Observations and Hydrological Modeling Using Constrained Ensemble Kalman Filtering

NASA Astrophysics Data System (ADS)

Pan, M.; Wood, E. F.

2004-05-01

This study explores a method to estimate various components of the water cycle (ET, runoff, land storage, etc.) based on a number of different info sources, including both observations and observation-enhanced model simulations. Different from existing data assimilations, this constrained Kalman filtering approach keeps the water budget perfectly closed while updating the states of the underlying model (VIC model) optimally using observations. Assimilating different data sources in this way has several advantages: (1) physical model is included to make estimation time series smooth, missing-free, and more physically consistent; (2) uncertainties in the model and observations are properly addressed; (3) model is constrained by observation thus to reduce model biases; (4) balance of water is always preserved along the assimilation. Experiments are carried out in Southern Great Plain region where necessary observations have been collected. This method may also be implemented in other applications with physical constraints (e.g. energy cycles) and at different scales.

Very Large Scale Optimization

NASA Technical Reports Server (NTRS)

Vanderplaats, Garrett; Townsend, James C. (Technical Monitor)

2002-01-01

The purpose of this research under the NASA Small Business Innovative Research program was to develop algorithms and associated software to solve very large nonlinear, constrained optimization tasks. Key issues included efficiency, reliability, memory, and gradient calculation requirements. This report describes the general optimization problem, ten candidate methods, and detailed evaluations of four candidates. The algorithm chosen for final development is a modern recreation of a 1960s external penalty function method that uses very limited computer memory and computational time. Although of lower efficiency, the new method can solve problems orders of magnitude larger than current methods. The resulting BIGDOT software has been demonstrated on problems with 50,000 variables and about 50,000 active constraints. For unconstrained optimization, it has solved a problem in excess of 135,000 variables. The method includes a technique for solving discrete variable problems that finds a "good" design, although a theoretical optimum cannot be guaranteed. It is very scalable in that the number of function and gradient evaluations does not change significantly with increased problem size. Test cases are provided to demonstrate the efficiency and reliability of the methods and software.

A novel constrained H2 optimization algorithm for mechatronics design in flexure-linked biaxial gantry.

PubMed

Ma, Jun; Chen, Si-Lu; Kamaldin, Nazir; Teo, Chek Sing; Tay, Arthur; Mamun, Abdullah Al; Tan, Kok Kiong

2017-11-01

The biaxial gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers from breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a flexure-linked biaxial gantry is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end-effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-to-point reference trajectories with same dynamics but different steps. From here, an H 2 optimization problem with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal step. Comparative experimental results obtained on the testbed are presented to verify the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

LCAMP: Location Constrained Approximate Message Passing for Compressed Sensing MRI

PubMed Central

Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

2016-01-01

Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random 1D signals and then applied to dynamic contrast-enhanced breast MRI to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5 - 10 seconds using Matlab) with highly undersampled 3D data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658

Robust Airfoil Optimization in High Resolution Design Space

NASA Technical Reports Server (NTRS)

Li, Wu; Padula, Sharon L.

2003-01-01

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables. Our experience with robust optimization indicates that our strategy produces reasonable airfoil shapes that are similar to the original airfoils, but these new shapes provide drag reduction over the specified range of Mach numbers. We have tested this strategy on a number of advanced airfoil models produced by knowledgeable aerodynamic design team members and found that our strategy produces airfoils better or equal to any designs produced by traditional design methods.

Wind Farm Turbine Type and Placement Optimization

NASA Astrophysics Data System (ADS)

Graf, Peter; Dykes, Katherine; Scott, George; Fields, Jason; Lunacek, Monte; Quick, Julian; Rethore, Pierre-Elouan

2016-09-01

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. This document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Wind farm turbine type and placement optimization

DOE PAGES

Graf, Peter; Dykes, Katherine; Scott, George; ...

2016-10-03

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Use of constrained optimization in the conceptual design of a medium-range subsonic transport

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1980-01-01

Constrained parameter optimization was used to perform the optimal conceptual design of a medium range transport configuration. The impact of choosing a given performance index was studied, and the required income for a 15 percent return on investment was proposed as a figure of merit. A number of design constants and constraint functions were systematically varied to document the sensitivities of the optimal design to a variety of economic and technological assumptions. A comparison was made for each of the parameter variations between the baseline configuration and the optimally redesigned configuration.

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, J; Chao, M

2016-06-15

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associatedmore » algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately improving tumor motion management for radiation therapy of cancer patients.« less

SU-E-T-551: Monitor Unit Optimization in Stereotactic Body Radiation Therapy for Stage I Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, B-T; Lu, J-Y

2015-06-15

Purpose: The study aims to reduce the monitor units (MUs) in the stereotactic body radiation therapy (SBRT) treatment for lung cancer by adjusting the optimizing parameters. Methods: Fourteen patients suffered from stage I Non-Small Cell Lung Cancer (NSCLC) were enrolled. Three groups of parameters were adjusted to investigate their effects on MU numbers and organs at risk (OARs) sparing: (1) the upper objective of planning target volume (UOPTV); (2) strength setting in the MU constraining objective; (3) max MU setting in the MU constraining objective. Results: We found that the parameters in the optimizer influenced the MU numbers in amore » priority, strength and max MU dependent manner. MU numbers showed a decreasing trend with the UOPTV increasing. MU numbers with low, medium and high priority for the UOPTV were 428±54, 312±48 and 258±31 MU/Gy, respectively. High priority for UOPTV also spared the heart, cord and lung while maintaining comparable PTV coverage than the low and medium priority group. It was observed that MU numbers tended to decrease with the strength increasing and max MU setting decreasing. With maximum strength, the MU numbers reached its minimum while maintaining comparable or improved dose to the normal tissues. It was also found that the MU numbers continued to decline at 85% and 75% max MU setting but no longer to decrease at 50% and 25%. Combined with high priority for UOPTV and MU constraining objectives, the MU numbers can be decreased as low as 223±26 MU/Gy. Conclusion:: The priority of UOPTV, MU constraining objective in the optimizer impact on the MU numbers in SBRT treatment for lung cancer. Giving high priority to the UOPTV, setting the strength to maximum value and the max MU to 50% in the MU objective achieves the lowest MU numbers while maintaining comparable or improved OAR sparing.« less

Top-down NOx and SO2 emissions simultaneously estimated from different OMI retrievals and inversion frameworks

NASA Astrophysics Data System (ADS)

Qu, Z.; Henze, D. K.; Wang, J.; Xu, X.; Wang, Y.

2017-12-01

Quantifying emissions trends of nitrogen oxides (NOx) and sulfur dioxide (SO2) is important for improving understanding of air pollution and the effectiveness of emission control strategies. We estimate long-term (2005-2016) global (2° x 2.5° resolution) and regional (North America and East Asia at 0.5° x 0.667° resolution) NOx emissions using a recently developed hybrid (mass-balance / 4D-Var) method with GEOS-Chem. NASA standard product and DOMINO retrievals of NO2 column are both used to constrain emissions; comparison of these results provides insight into regions where trends are most robust with respect to retrieval uncertainties, and highlights regions where seemingly significant trends are retrieval-specific. To incorporate chemical interactions among species, we extend our hybrid method to assimilate NO2 and SO2 observations and optimize NOx and SO2 emissions simultaneously. Due to chemical interactions, inclusion of SO2 observations leads to 30% grid-scale differences in posterior NOx emissions compared to those constrained only by NO2 observations. When assimilating and optimizing both species in pseudo observation tests, the sum of the normalized mean squared error (compared to the true emissions) of NOx and SO2 posterior emissions are 54-63% smaller than when observing/constraining a single species. NOx and SO2 emissions are also correlated through the amount of fuel combustion. To incorporate this correlation into the inversion, we optimize seven sector-specific emission scaling factors, including industry, energy, residential, aviation, transportation, shipping and agriculture. We compare posterior emissions from inversions optimizing only species' emissions, only sector-based emissions, and both species' and sector-based emissions. In situ measurements of NOx and SO2 are applied to evaluate the performance of these inversions. The impacts of the inversion on PM2.5 and O3 concentrations and premature deaths are also evaluated.

A Composite Algorithm for Mixed Integer Constrained Nonlinear Optimization.

DTIC Science & Technology

1980-01-01

de Silva [141, and Weisman and Wood [76). A particular direct search algorithm, the simplex method, has been cited for having the potential for...spaced discrete points on a line which makes the direction suitable for an efficient integer search technique based on Fibonacci numbers. Two...defined by a subset of variables. The complex algorithm is particularly well suited for this subspace search for two reasons. First, the complex method

Bounding the moment deficit rate on crustal faults using geodetic data: Methods

DOE PAGES

Maurer, Jeremy; Segall, Paul; Bradley, Andrew Michael

2017-08-19

Here, the geodetically derived interseismic moment deficit rate (MDR) provides a first-order constraint on earthquake potential and can play an important role in seismic hazard assessment, but quantifying uncertainty in MDR is a challenging problem that has not been fully addressed. We establish criteria for reliable MDR estimators, evaluate existing methods for determining the probability density of MDR, and propose and evaluate new methods. Geodetic measurements moderately far from the fault provide tighter constraints on MDR than those nearby. Previously used methods can fail catastrophically under predictable circumstances. The bootstrap method works well with strong data constraints on MDR, butmore » can be strongly biased when network geometry is poor. We propose two new methods: the Constrained Optimization Bounding Estimator (COBE) assumes uniform priors on slip rate (from geologic information) and MDR, and can be shown through synthetic tests to be a useful, albeit conservative estimator; the Constrained Optimization Bounding Linear Estimator (COBLE) is the corresponding linear estimator with Gaussian priors rather than point-wise bounds on slip rates. COBE matches COBLE with strong data constraints on MDR. We compare results from COBE and COBLE to previously published results for the interseismic MDR at Parkfield, on the San Andreas Fault, and find similar results; thus, the apparent discrepancy between MDR and the total moment release (seismic and afterslip) in the 2004 Parkfield earthquake remains.« less

Bounding the moment deficit rate on crustal faults using geodetic data: Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maurer, Jeremy; Segall, Paul; Bradley, Andrew Michael

Here, the geodetically derived interseismic moment deficit rate (MDR) provides a first-order constraint on earthquake potential and can play an important role in seismic hazard assessment, but quantifying uncertainty in MDR is a challenging problem that has not been fully addressed. We establish criteria for reliable MDR estimators, evaluate existing methods for determining the probability density of MDR, and propose and evaluate new methods. Geodetic measurements moderately far from the fault provide tighter constraints on MDR than those nearby. Previously used methods can fail catastrophically under predictable circumstances. The bootstrap method works well with strong data constraints on MDR, butmore » can be strongly biased when network geometry is poor. We propose two new methods: the Constrained Optimization Bounding Estimator (COBE) assumes uniform priors on slip rate (from geologic information) and MDR, and can be shown through synthetic tests to be a useful, albeit conservative estimator; the Constrained Optimization Bounding Linear Estimator (COBLE) is the corresponding linear estimator with Gaussian priors rather than point-wise bounds on slip rates. COBE matches COBLE with strong data constraints on MDR. We compare results from COBE and COBLE to previously published results for the interseismic MDR at Parkfield, on the San Andreas Fault, and find similar results; thus, the apparent discrepancy between MDR and the total moment release (seismic and afterslip) in the 2004 Parkfield earthquake remains.« less

Method for hue plane preserving color correction.

PubMed

Mackiewicz, Michal; Andersen, Casper F; Finlayson, Graham

2016-11-01

Hue plane preserving color correction (HPPCC), introduced by Andersen and Hardeberg [Proceedings of the 13th Color and Imaging Conference (CIC) (2005), pp. 141-146], maps device-dependent color values (RGB) to colorimetric color values (XYZ) using a set of linear transforms, realized by white point preserving 3×3 matrices, where each transform is learned and applied in a subregion of color space, defined by two adjacent hue planes. The hue plane delimited subregions of camera RGB values are mapped to corresponding hue plane delimited subregions of estimated colorimetric XYZ values. Hue planes are geometrical half-planes, where each is defined by the neutral axis and a chromatic color in a linear color space. The key advantage of the HPPCC method is that, while offering an estimation accuracy of higher order methods, it maintains the linear colorimetric relations of colors in hue planes. As a significant result, it therefore also renders the colorimetric estimates invariant to exposure and shading of object reflection. In this paper, we present a new flexible and robust version of HPPCC using constrained least squares in the optimization, where the subregions can be chosen freely in number and position in order to optimize the results while constraining transform continuity at the subregion boundaries. The method is compared to a selection of other state-of-the-art characterization methods, and the results show that it outperforms the original HPPCC method.

Hierarchical Bayesian Model Averaging for Chance Constrained Remediation Designs

NASA Astrophysics Data System (ADS)

Chitsazan, N.; Tsai, F. T.

2012-12-01

Groundwater remediation designs are heavily relying on simulation models which are subjected to various sources of uncertainty in their predictions. To develop a robust remediation design, it is crucial to understand the effect of uncertainty sources. In this research, we introduce a hierarchical Bayesian model averaging (HBMA) framework to segregate and prioritize sources of uncertainty in a multi-layer frame, where each layer targets a source of uncertainty. The HBMA framework provides an insight to uncertainty priorities and propagation. In addition, HBMA allows evaluating model weights in different hierarchy levels and assessing the relative importance of models in each level. To account for uncertainty, we employ a chance constrained (CC) programming for stochastic remediation design. Chance constrained programming was implemented traditionally to account for parameter uncertainty. Recently, many studies suggested that model structure uncertainty is not negligible compared to parameter uncertainty. Using chance constrained programming along with HBMA can provide a rigorous tool for groundwater remediation designs under uncertainty. In this research, the HBMA-CC was applied to a remediation design in a synthetic aquifer. The design was to develop a scavenger well approach to mitigate saltwater intrusion toward production wells. HBMA was employed to assess uncertainties from model structure, parameter estimation and kriging interpolation. An improved harmony search optimization method was used to find the optimal location of the scavenger well. We evaluated prediction variances of chloride concentration at the production wells through the HBMA framework. The results showed that choosing the single best model may lead to a significant error in evaluating prediction variances for two reasons. First, considering the single best model, variances that stem from uncertainty in the model structure will be ignored. Second, considering the best model with non-dominant model weight may underestimate or overestimate prediction variances by ignoring other plausible propositions. Chance constraints allow developing a remediation design with a desirable reliability. However, considering the single best model, the calculated reliability will be different from the desirable reliability. We calculated the reliability of the design for the models at different levels of HBMA. The results showed that by moving toward the top layers of HBMA, the calculated reliability converges to the chosen reliability. We employed the chance constrained optimization along with the HBMA framework to find the optimal location and pumpage for the scavenger well. The results showed that using models at different levels in the HBMA framework, the optimal location of the scavenger well remained the same, but the optimal extraction rate was altered. Thus, we concluded that the optimal pumping rate was sensitive to the prediction variance. Also, the prediction variance was changed by using different extraction rate. Using very high extraction rate will cause prediction variances of chloride concentration at the production wells to approach zero regardless of which HBMA models used.

Active and Reactive Power Optimal Dispatch Associated with Load and DG Uncertainties in Active Distribution Network

NASA Astrophysics Data System (ADS)

Gao, F.; Song, X. H.; Zhang, Y.; Li, J. F.; Zhao, S. S.; Ma, W. Q.; Jia, Z. Y.

2017-05-01

In order to reduce the adverse effects of uncertainty on optimal dispatch in active distribution network, an optimal dispatch model based on chance-constrained programming is proposed in this paper. In this model, the active and reactive power of DG can be dispatched at the aim of reducing the operating cost. The effect of operation strategy on the cost can be reflected in the objective which contains the cost of network loss, DG curtailment, DG reactive power ancillary service, and power quality compensation. At the same time, the probabilistic constraints can reflect the operation risk degree. Then the optimal dispatch model is simplified as a series of single stage model which can avoid large variable dimension and improve the convergence speed. And the single stage model is solved using a combination of particle swarm optimization (PSO) and point estimate method (PEM). Finally, the proposed optimal dispatch model and method is verified by the IEEE33 test system.

Interplanetary program to optimize simulated trajectories (IPOST). Volume 4: Sample cases

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Simulated Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization are performed using the Standard NPSOL algorithm. The IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graf, Peter; Dykes, Katherine; Scott, George

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

PubMed

Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

2018-02-01

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

NASA Astrophysics Data System (ADS)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Chance-Constrained Guidance With Non-Convex Constraints

NASA Technical Reports Server (NTRS)

Ono, Masahiro

2011-01-01

Missions to small bodies, such as comets or asteroids, require autonomous guidance for descent to these small bodies. Such guidance is made challenging by uncertainty in the position and velocity of the spacecraft, as well as the uncertainty in the gravitational field around the small body. In addition, the requirement to avoid collision with the asteroid represents a non-convex constraint that means finding the optimal guidance trajectory, in general, is intractable. In this innovation, a new approach is proposed for chance-constrained optimal guidance with non-convex constraints. Chance-constrained guidance takes into account uncertainty so that the probability of collision is below a specified threshold. In this approach, a new bounding method has been developed to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. Branch and bound enables non-convex problems to be solved efficiently to global optimality. Considering the problem of finite-horizon robust optimal control of dynamic systems under Gaussian-distributed stochastic uncertainty, with state and control constraints, a discrete-time, continuous-state linear dynamics model is assumed. Gaussian-distributed stochastic uncertainty is a more natural model for exogenous disturbances such as wind gusts and turbulence than the previously studied set-bounded models. However, with stochastic uncertainty, it is often impossible to guarantee that state constraints are satisfied, because there is typically a non-zero probability of having a disturbance that is large enough to push the state out of the feasible region. An effective framework to address robustness with stochastic uncertainty is optimization with chance constraints. These require that the probability of violating the state constraints (i.e., the probability of failure) is below a user-specified bound known as the risk bound. An example problem is to drive a car to a destination as fast as possible while limiting the probability of an accident to 10(exp -7). This framework allows users to trade conservatism against performance by choosing the risk bound. The more risk the user accepts, the better performance they can expect.

Prepositioning emergency supplies under uncertainty: a parametric optimization method

NASA Astrophysics Data System (ADS)

Bai, Xuejie; Gao, Jinwu; Liu, Yankui

2018-07-01

Prepositioning of emergency supplies is an effective method for increasing preparedness for disasters and has received much attention in recent years. In this article, the prepositioning problem is studied by a robust parametric optimization method. The transportation cost, supply, demand and capacity are unknown prior to the extraordinary event, which are represented as fuzzy parameters with variable possibility distributions. The variable possibility distributions are obtained through the credibility critical value reduction method for type-2 fuzzy variables. The prepositioning problem is formulated as a fuzzy value-at-risk model to achieve a minimum total cost incurred in the whole process. The key difficulty in solving the proposed optimization model is to evaluate the quantile of the fuzzy function in the objective and the credibility in the constraints. The objective function and constraints can be turned into their equivalent parametric forms through chance constrained programming under the different confidence levels. Taking advantage of the structural characteristics of the equivalent optimization model, a parameter-based domain decomposition method is developed to divide the original optimization problem into six mixed-integer parametric submodels, which can be solved by standard optimization solvers. Finally, to explore the viability of the developed model and the solution approach, some computational experiments are performed on realistic scale case problems. The computational results reported in the numerical example show the credibility and superiority of the proposed parametric optimization method.

Method for exploiting bias in factor analysis using constrained alternating least squares algorithms

DOEpatents

Keenan, Michael R.

2008-12-30

Bias plays an important role in factor analysis and is often implicitly made use of, for example, to constrain solutions to factors that conform to physical reality. However, when components are collinear, a large range of solutions may exist that satisfy the basic constraints and fit the data equally well. In such cases, the introduction of mathematical bias through the application of constraints may select solutions that are less than optimal. The biased alternating least squares algorithm of the present invention can offset mathematical bias introduced by constraints in the standard alternating least squares analysis to achieve factor solutions that are most consistent with physical reality. In addition, these methods can be used to explicitly exploit bias to provide alternative views and provide additional insights into spectral data sets.

$L^1$ penalization of volumetric dose objectives in optimal control of PDEs

DOE PAGES

Barnard, Richard C.; Clason, Christian

2017-02-11

This work is concerned with a class of PDE-constrained optimization problems that are motivated by an application in radiotherapy treatment planning. Here the primary design objective is to minimize the volume where a functional of the state violates a prescribed level, but prescribing these levels in the form of pointwise state constraints leads to infeasible problems. We therefore propose an alternative approach based on L 1 penalization of the violation that is also applicable when state constraints are infeasible. We establish well-posedness of the corresponding optimal control problem, derive first-order optimality conditions, discuss convergence of minimizers as the penalty parametermore » tends to infinity, and present a semismooth Newton method for their efficient numerical solution. Finally, the performance of this method for a model problem is illustrated and contrasted with an alternative approach based on (regularized) state constraints.« less

Configuration-shape-size optimization of space structures by material redistribution

NASA Technical Reports Server (NTRS)

Vandenbelt, D. N.; Crivelli, L. A.; Felippa, C. A.

1993-01-01

This project investigates the configuration-shape-size optimization (CSSO) of orbiting and planetary space structures. The project embodies three phases. In the first one the material-removal CSSO method introduced by Kikuchi and Bendsoe (KB) is further developed to gain understanding of finite element homogenization techniques as well as associated constrained optimization algorithms that must carry along a very large number (thousands) of design variables. In the CSSO-KB method an optimal structure is 'carved out' of a design domain initially filled with finite elements, by allowing perforations (microholes) to develop, grow and merge. The second phase involves 'materialization' of space structures from the void, thus reversing the carving process. The third phase involves analysis of these structures for construction and operational constraints, with emphasis in packaging and deployment. The present paper describes progress in selected areas of the first project phase and the start of the second one.

Stress-Constrained Structural Topology Optimization with Design-Dependent Loads

NASA Astrophysics Data System (ADS)

Lee, Edmund

Topology optimization is commonly used to distribute a given amount of material to obtain the stiffest structure, with predefined fixed loads. The present work investigates the result of applying stress constraints to topology optimization, for problems with design-depending loading, such as self-weight and pressure. In order to apply pressure loading, a material boundary identification scheme is proposed, iteratively connecting points of equal density. In previous research, design-dependent loading problems have been limited to compliance minimization. The present study employs a more practical approach by minimizing mass subject to failure constraints, and uses a stress relaxation technique to avoid stress constraint singularities. The results show that these design dependent loading problems may converge to a local minimum when stress constraints are enforced. Comparisons between compliance minimization solutions and stress-constrained solutions are also given. The resulting topologies of these two solutions are usually vastly different, demonstrating the need for stress-constrained topology optimization.

Nonlinear programming extensions to rational function approximations of unsteady aerodynamics

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1987-01-01

This paper deals with approximating unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft. Two methods of formulating these approximations are extended to include both the same flexibility in constraining them and the same methodology in optimizing nonlinear parameters as another currently used 'extended least-squares' method. Optimal selection of 'nonlinear' parameters is made in each of the three methods by use of the same nonlinear (nongradient) optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is of lower order than that required when no optimization of the nonlinear terms is performed. The free 'linear' parameters are determined using least-squares matrix techniques on a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from the different approaches are described, and results are presented which show comparative evaluations from application of each of the extended methods to a numerical example. The results obtained for the example problem show a significant (up to 63 percent) reduction in the number of differential equations used to represent the unsteady aerodynamic forces in linear time-invariant equations of motion as compared to a conventional method in which nonlinear terms are not optimized.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

Formulation of image fusion as a constrained least squares optimization problem

PubMed Central

Dwork, Nicholas; Lasry, Eric M.; Pauly, John M.; Balbás, Jorge

2017-01-01

Abstract. Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem. PMID:28331885

True logarithmic amplification of frequency clock in SS-OCT for calibration

PubMed Central

Liu, Bin; Azimi, Ehsan; Brezinski, Mark E.

2011-01-01

With swept source optical coherence tomography (SS-OCT), imprecise signal calibration prevents optimal imaging of biological tissues such as coronary artery. This work demonstrates an approach using a true logarithmic amplifier to precondition the clock signal, with the effort to minimize the noises and phase errors for optimal calibration. This method was validated and tested with a high-speed SS-OCT. The experimental results manifest its superior ability on optimization of the calibration and improvement of the imaging performance. Particularly, this hardware-based approach is suitable for real-time calibration in a high-speed system where computation time is constrained. PMID:21698036

Fitting aerodynamic forces in the Laplace domain: An application of a nonlinear nongradient technique to multilevel constrained optimization

NASA Technical Reports Server (NTRS)

Tiffany, S. H.; Adams, W. M., Jr.

1984-01-01

A technique which employs both linear and nonlinear methods in a multilevel optimization structure to best approximate generalized unsteady aerodynamic forces for arbitrary motion is described. Optimum selection of free parameters is made in a rational function approximation of the aerodynamic forces in the Laplace domain such that a best fit is obtained, in a least squares sense, to tabular data for purely oscillatory motion. The multilevel structure and the corresponding formulation of the objective models are presented which separate the reduction of the fit error into linear and nonlinear problems, thus enabling the use of linear methods where practical. Certain equality and inequality constraints that may be imposed are identified; a brief description of the nongradient, nonlinear optimizer which is used is given; and results which illustrate application of the method are presented.

Adaptive eigenspace method for inverse scattering problems in the frequency domain

NASA Astrophysics Data System (ADS)

Grote, Marcus J.; Kray, Marie; Nahum, Uri

2017-02-01

A nonlinear optimization method is proposed for the solution of inverse scattering problems in the frequency domain, when the scattered field is governed by the Helmholtz equation. The time-harmonic inverse medium problem is formulated as a PDE-constrained optimization problem and solved by an inexact truncated Newton-type iteration. Instead of a grid-based discrete representation, the unknown wave speed is projected to a particular finite-dimensional basis of eigenfunctions, which is iteratively adapted during the optimization. Truncating the adaptive eigenspace (AE) basis at a (small and slowly increasing) finite number of eigenfunctions effectively introduces regularization into the inversion and thus avoids the need for standard Tikhonov-type regularization. Both analytical and numerical evidence underpins the accuracy of the AE representation. Numerical experiments demonstrate the efficiency and robustness to missing or noisy data of the resulting adaptive eigenspace inversion method.

Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization

DTIC Science & Technology

2016-11-22

structure of the graph, we replace the ℓ1- norm by the nonconvex Capped -ℓ1 norm , and obtain the Generalized Capped -ℓ1 regularized logistic regression...X. M. Yuan. Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization. Mathematics of Computation, 82(281):301...better approximations of ℓ0- norm theoretically and computationally beyond ℓ1- norm , for example, the compressive sensing (Xiao et al., 2011). The

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watkins, W.T.; Siebers, J.V.

Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanarmore » Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing significant variations in OAR doses including mean dose reductions >5 Gy. Clinical implementation will facilitate patient-specific decision making based on achievable dosimetry as opposed to accept/reject models based on population derived objectives.« less

Learning optimal embedded cascades.

PubMed

Saberian, Mohammad Javad; Vasconcelos, Nuno

2012-10-01

The problem of automatic and optimal design of embedded object detector cascades is considered. Two main challenges are identified: optimization of the cascade configuration and optimization of individual cascade stages, so as to achieve the best tradeoff between classification accuracy and speed, under a detection rate constraint. Two novel boosting algorithms are proposed to address these problems. The first, RCBoost, formulates boosting as a constrained optimization problem which is solved with a barrier penalty method. The constraint is the target detection rate, which is met at all iterations of the boosting process. This enables the design of embedded cascades of known configuration without extensive cross validation or heuristics. The second, ECBoost, searches over cascade configurations to achieve the optimal tradeoff between classification risk and speed. The two algorithms are combined into an overall boosting procedure, RCECBoost, which optimizes both the cascade configuration and its stages under a detection rate constraint, in a fully automated manner. Extensive experiments in face, car, pedestrian, and panda detection show that the resulting detectors achieve an accuracy versus speed tradeoff superior to those of previous methods.

Imparting Desired Attributes by Optimization in Structural Design

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2003-01-01

Commonly available optimization methods typically produce a single optimal design as a Constrained minimum of a particular objective function. However, in engineering design practice it is quite often important to explore as much of the design space as possible with respect to many attributes to find out what behaviors are possible and not possible within the initially adopted design concept. The paper shows that the very simple method of the sum of objectives is useful for such exploration. By geometrical argument it is demonstrated that if every weighting coefficient is allowed to change its magnitude and its sign then the method returns a set of designs that are all feasible, diverse in their attributes, and include the Pareto and non-Pareto solutions, at least for convex cases. Numerical examples in the paper include a case of an aircraft wing structural box with thousands of degrees of freedom and constraints, and over 100 design variables, whose attributes are structural mass, volume, displacement, and frequency. The method is inherently suitable for parallel, coarse-grained implementation that enables exploration of the design space in the elapsed time of a single structural optimization.

Self-consistent collective coordinate for reaction path and inertial mass

NASA Astrophysics Data System (ADS)

Wen, Kai; Nakatsukasa, Takashi

2016-11-01

We propose a numerical method to determine the optimal collective reaction path for a nucleus-nucleus collision, based on the adiabatic self-consistent collective coordinate (ASCC) method. We use an iterative method, combining the imaginary-time evolution and the finite amplitude method, for the solution of the ASCC coupled equations. It is applied to the simplest case, α -α scattering. We determine the collective path, the potential, and the inertial mass. The results are compared with other methods, such as the constrained Hartree-Fock method, Inglis's cranking formula, and the adiabatic time-dependent Hartree-Fock (ATDHF) method.

Partial differential equations constrained combinatorial optimization on an adiabatic quantum computer

NASA Astrophysics Data System (ADS)

Chandra, Rishabh

Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.

Trajectory optimization and guidance law development for national aerospace plane applications

NASA Technical Reports Server (NTRS)

Calise, A. J.; Flandro, G. A.; Corban, J. E.

1988-01-01

The work completed to date is comprised of the following: a simple vehicle model representative of the aerospace plane concept in the hypersonic flight regime, fuel-optimal climb profiles for the unconstrained and dynamic pressure constrained cases generated using a reduced order dynamic model, an analytic switching condition for transition to rocket powered flight as orbital velocity is approached, simple feedback guidance laws for both the unconstrained and dynamic pressure constrained cases derived via singular perturbation theory and a nonlinear transformation technique, and numerical simulation results for ascent to orbit in the dynamic pressure constrained case.

Application of augmented-Lagrangian methods in meteorology: Comparison of different conjugate-gradient codes for large-scale minimization

NASA Technical Reports Server (NTRS)

Navon, I. M.

1984-01-01

A Lagrange multiplier method using techniques developed by Bertsekas (1982) was applied to solving the problem of enforcing simultaneous conservation of the nonlinear integral invariants of the shallow water equations on a limited area domain. This application of nonlinear constrained optimization is of the large dimensional type and the conjugate gradient method was found to be the only computationally viable method for the unconstrained minimization. Several conjugate-gradient codes were tested and compared for increasing accuracy requirements. Robustness and computational efficiency were the principal criteria.

On meeting capital requirements with a chance-constrained optimization model.

PubMed

Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

2016-01-01

This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

Robust Constrained Optimization Approach to Control Design for International Space Station Centrifuge Rotor Auto Balancing Control System

NASA Technical Reports Server (NTRS)

Postma, Barry Dirk

2005-01-01

This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. The design goal is to minimize a performance objective of the system, while guaranteeing stability and proper performance for a range of uncertain plants. The Performance objective is to minimize the translational response of the centrifuge rotor due to a fixed worst-case rotor imbalance. The robustness constraints are posed with respect to parametric uncertainty in the plant. The proposed approach to control design allows for both of these objectives to be handled within the framework of constrained optimization. The resulting controller achieves acceptable performance and robustness characteristics.

Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Zhang, Fan; Guo, Shanshan; Liu, Xiao; Guo, Ping

2018-01-01

An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.

Quadratic constrained mixed discrete optimization with an adiabatic quantum optimizer

NASA Astrophysics Data System (ADS)

Chandra, Rishabh; Jacobson, N. Tobias; Moussa, Jonathan E.; Frankel, Steven H.; Kais, Sabre

2014-07-01

We extend the family of problems that may be implemented on an adiabatic quantum optimizer (AQO). When a quadratic optimization problem has at least one set of discrete controls and the constraints are linear, we call this a quadratic constrained mixed discrete optimization (QCMDO) problem. QCMDO problems are NP-hard, and no efficient classical algorithm for their solution is known. Included in the class of QCMDO problems are combinatorial optimization problems constrained by a linear partial differential equation (PDE) or system of linear PDEs. An essential complication commonly encountered in solving this type of problem is that the linear constraint may introduce many intermediate continuous variables into the optimization while the computational cost grows exponentially with problem size. We resolve this difficulty by developing a constructive mapping from QCMDO to quadratic unconstrained binary optimization (QUBO) such that the size of the QUBO problem depends only on the number of discrete control variables. With a suitable embedding, taking into account the physical constraints of the realizable coupling graph, the resulting QUBO problem can be implemented on an existing AQO. The mapping itself is efficient, scaling cubically with the number of continuous variables in the general case and linearly in the PDE case if an efficient preconditioner is available.

Critical transition in the constrained traveling salesman problem.

PubMed

Andrecut, M; Ali, M K

2001-04-01

We investigate the finite size scaling of the mean optimal tour length as a function of density of obstacles in a constrained variant of the traveling salesman problem (TSP). The computational experience pointed out a critical transition (at rho(c) approximately 85%) in the dependence between the excess of the mean optimal tour length over the Held-Karp lower bound and the density of obstacles.

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

NASA Astrophysics Data System (ADS)

Wang, Yunhua; DeBrunner, Linda; DeBrunner, Victor; Zhou, Dayong

2008-12-01

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

NASA Astrophysics Data System (ADS)

Masalmah, Yahya M.; Vélez-Reyes, Miguel

2007-04-01

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

PubMed

Lin, Yu-Hsiu; Hu, Yu-Chen

2018-04-27

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Solution of nonlinear multivariable constrained systems using a gradient projection digital algorithm that is insensitive to the initial state

NASA Technical Reports Server (NTRS)

Hargrove, A.

1982-01-01

Optimal digital control of nonlinear multivariable constrained systems was studied. The optimal controller in the form of an algorithm was improved and refined by reducing running time and storage requirements. A particularly difficult system of nine nonlinear state variable equations was chosen as a test problem for analyzing and improving the controller. Lengthy analysis, modeling, computing and optimization were accomplished. A remote interactive teletype terminal was installed. Analysis requiring computer usage of short duration was accomplished using Tuskegee's VAX 11/750 system.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Resolution improvement in positron emission tomography using anatomical Magnetic Resonance Imaging.

PubMed

Chu, Yong; Su, Min-Ying; Mandelkern, Mark; Nalcioglu, Orhan

2006-08-01

An ideal imaging system should provide information with high-sensitivity, high spatial, and temporal resolution. Unfortunately, it is not possible to satisfy all of these desired features in a single modality. In this paper, we discuss methods to improve the spatial resolution in positron emission imaging (PET) using a priori information from Magnetic Resonance Imaging (MRI). Our approach uses an image restoration algorithm based on the maximization of mutual information (MMI), which has found significant success for optimizing multimodal image registration. The MMI criterion is used to estimate the parameters in the Sharpness-Constrained Wiener filter. The generated filter is then applied to restore PET images of a realistic digital brain phantom. The resulting restored images show improved resolution and better signal-to-noise ratio compared to the interpolated PET images. We conclude that a Sharpness-Constrained Wiener filter having parameters optimized from a MMI criterion may be useful for restoring spatial resolution in PET based on a priori information from correlated MRI.

Human health risk constrained naphthalene-contaminated groundwater remediation management through an improved credibility method.

PubMed

Li, Jing; Lu, Hongwei; Fan, Xing; Chen, Yizhong

2017-07-01

In this study, a human health risk constrained groundwater remediation management program based on the improved credibility is developed for naphthalene contamination. The program integrates simulation, multivariate regression analysis, health risk assessment, uncertainty analysis, and nonlinear optimization into a general framework. The improved credibility-based optimization model for groundwater remediation management with consideration of human health risk (ICOM-HHR) is capable of not only effectively addressing parameter uncertainties and risk-exceeding possibility in human health risk but also providing a credibility level that indicates the satisfaction of the optimal groundwater remediation strategies with multiple contributions of possibility and necessity. The capabilities and effectiveness of ICOM-HHR are illustrated through a real-world case study in Anhui Province, China. Results indicate that the ICOM-HHR would generate double remediation cost yet reduce approximately 10 times of the naphthalene concentrations at monitoring wells, i.e., mostly less than 1 μg/L, which implies that the ICOM-HHR usually results in better environmental and health risk benefits. And it is acceptable to obtain a better environmental quality and a lower health risk level with sacrificing a certain economic benefit.

An iterative algorithm for L1-TV constrained regularization in image restoration

NASA Astrophysics Data System (ADS)

Chen, K.; Loli Piccolomini, E.; Zama, F.

2015-11-01

We consider the problem of restoring blurred images affected by impulsive noise. The adopted method restores the images by solving a sequence of constrained minimization problems where the data fidelity function is the ℓ1 norm of the residual and the constraint, chosen as the image Total Variation, is automatically adapted to improve the quality of the restored images. Although this approach is general, we report here the case of vectorial images where the blurring model involves contributions from the different image channels (cross channel blur). A computationally convenient extension of the Total Variation function to vectorial images is used and the results reported show that this approach is efficient for recovering nearly optimal images.

Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

NASA Astrophysics Data System (ADS)

Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

2017-12-01

In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

Inverse lithography using sparse mask representations

NASA Astrophysics Data System (ADS)

Ionescu, Radu C.; Hurley, Paul; Apostol, Stefan

2015-03-01

We present a novel optimisation algorithm for inverse lithography, based on optimization of the mask derivative, a domain inherently sparse, and for rectilinear polygons, invertible. The method is first developed assuming a point light source, and then extended to general incoherent sources. What results is a fast algorithm, producing manufacturable masks (the search space is constrained to rectilinear polygons), and flexible (specific constraints such as minimal line widths can be imposed). One inherent trick is to treat polygons as continuous entities, thus making aerial image calculation extremely fast and accurate. Requirements for mask manufacturability can be integrated in the optimization without too much added complexity. We also explain how to extend the scheme for phase-changing mask optimization.

Target detection in GPR data using joint low-rank and sparsity constraints

NASA Astrophysics Data System (ADS)

Bouzerdoum, Abdesselam; Tivive, Fok Hing Chi; Abeynayake, Canicious

2016-05-01

In ground penetrating radars, background clutter, which comprises the signals backscattered from the rough, uneven ground surface and the background noise, impairs the visualization of buried objects and subsurface inspections. In this paper, a clutter mitigation method is proposed for target detection. The removal of background clutter is formulated as a constrained optimization problem to obtain a low-rank matrix and a sparse matrix. The low-rank matrix captures the ground surface reflections and the background noise, whereas the sparse matrix contains the target reflections. An optimization method based on split-Bregman algorithm is developed to estimate these two matrices from the input GPR data. Evaluated on real radar data, the proposed method achieves promising results in removing the background clutter and enhancing the target signature.

String-averaging incremental subgradients for constrained convex optimization with applications to reconstruction of tomographic images

NASA Astrophysics Data System (ADS)

Massambone de Oliveira, Rafael; Salomão Helou, Elias; Fontoura Costa, Eduardo

2016-11-01

We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is split into sequences (strings) and a given iterate is processed independently along each string, possibly in parallel, by an incremental subgradient method (ISM). The end-points of all strings are averaged to form the next iterate. The method is useful to solve sparse and large-scale non-smooth convex optimization problems, such as those arising in tomographic imaging. A convergence analysis is provided under realistic, standard conditions. Numerical tests are performed in a tomographic image reconstruction application, showing good performance for the convergence speed when measured as the decrease ratio of the objective function, in comparison to classical ISM.

QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.

PubMed

Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results.

On optimal infinite impulse response edge detection filters

NASA Technical Reports Server (NTRS)

Sarkar, Sudeep; Boyer, Kim L.

1991-01-01

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

Constrained multi-objective optimization of storage ring lattices

NASA Astrophysics Data System (ADS)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

Development of optimal models of porous media by combining static and dynamic data: the permeability and porosity distributions.

PubMed

Hamzehpour, Hossein; Rasaei, M Reza; Sahimi, Muhammad

2007-05-01

We describe a method for the development of the optimal spatial distributions of the porosity phi and permeability k of a large-scale porous medium. The optimal distributions are constrained by static and dynamic data. The static data that we utilize are limited data for phi and k, which the method honors in the optimal model and utilizes their correlation functions in the optimization process. The dynamic data include the first-arrival (FA) times, at a number of receivers, of seismic waves that have propagated in the porous medium, and the time-dependent production rates of a fluid that flows in the medium. The method combines the simulated-annealing method with a simulator that solves numerically the three-dimensional (3D) acoustic wave equation and computes the FA times, and a second simulator that solves the 3D governing equation for the fluid's pressure as a function of time. To our knowledge, this is the first time that an optimization method has been developed to determine simultaneously the global minima of two distinct total energy functions. As a stringent test of the method's accuracy, we solve for flow of two immiscible fluids in the same porous medium, without using any data for the two-phase flow problem in the optimization process. We show that the optimal model, in addition to honoring the data, also yields accurate spatial distributions of phi and k, as well as providing accurate quantitative predictions for the single- and two-phase flow problems. The efficiency of the computations is discussed in detail.

Thermally-Constrained Fuel-Optimal ISS Maneuvers

NASA Technical Reports Server (NTRS)

Bhatt, Sagar; Svecz, Andrew; Alaniz, Abran; Jang, Jiann-Woei; Nguyen, Louis; Spanos, Pol

2015-01-01

Optimal Propellant Maneuvers (OPMs) are now being used to rotate the International Space Station (ISS) and have saved hundreds of kilograms of propellant over the last two years. The savings are achieved by commanding the ISS to follow a pre-planned attitude trajectory optimized to take advantage of environmental torques. The trajectory is obtained by solving an optimal control problem. Prior to use on orbit, OPM trajectories are screened to ensure a static sun vector (SSV) does not occur during the maneuver. The SSV is an indicator that the ISS hardware temperatures may exceed thermal limits, causing damage to the components. In this paper, thermally-constrained fuel-optimal trajectories are presented that avoid an SSV and can be used throughout the year while still reducing propellant consumption significantly.

A Proposal of a Fast Computation Method for Thermal Capacity and Voltage ATC by Means of Homotopy Functions

NASA Astrophysics Data System (ADS)

Zoka, Yoshifumi; Yorino, Naoto; Kawano, Koki; Suenari, Hiroyasu

This paper proposes a fast computation method for Available Transfer Capability (ATC) with respect to thermal and voltage magnitude limits. In the paper, ATC is formulated as an optimization problem. In order to obtain the efficiency for the N-1 outage contingency calculations, linear sensitivity methods are applied for screening and ranking all contingency selections with respect to the thermal and voltage magnitude limits margin to identify the severest case. In addition, homotopy functions are used for the generator QV constrains to reduce the maximum error of the linear estimation. Then, the Primal-Dual Interior Point Method (PDIPM) is used to solve the optimization problem for the severest case only, in which the solutions of ATC can be obtained efficiently. The effectiveness of the proposed method is demonstrated through IEEE 30, 57, 118-bus systems.

Robust optimal control of material flows in demand-driven supply networks

NASA Astrophysics Data System (ADS)

Laumanns, Marco; Lefeber, Erjen

2006-04-01

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

Optimal design of dampers within seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Qian, Hui; Song, Wali; Wang, Liqiang

2009-07-01

An improved multi-objective genetic algorithm for structural passive control system optimization is proposed. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. For a constrained problem, the dominance-based penalty function method is advanced, containing information on an individual's status (feasible or infeasible), position in a search space, and distance from a Pareto optimal set. The proposed approach is used for the optimal designs of a six-storey building with shape memory alloy dampers subjected to earthquake. The number and position of dampers are chosen as the design variables. The number of dampers and peak relative inter-storey drift are considered as the objective functions. Numerical results generate a set of non-dominated solutions.

Model and algorithm based on accurate realization of dwell time in magnetorheological finishing.

PubMed

Song, Ci; Dai, Yifan; Peng, Xiaoqiang

2010-07-01

Classically, a dwell-time map is created with a method such as deconvolution or numerical optimization, with the input being a surface error map and influence function. This dwell-time map is the numerical optimum for minimizing residual form error, but it takes no account of machine dynamics limitations. The map is then reinterpreted as machine speeds and accelerations or decelerations in a separate operation. In this paper we consider combining the two methods in a single optimization by the use of a constrained nonlinear optimization model, which regards both the two-norm of the surface residual error and the dwell-time gradient as an objective function. This enables machine dynamic limitations to be properly considered within the scope of the optimization, reducing both residual surface error and polishing times. Further simulations are introduced to demonstrate the feasibility of the model, and the velocity map is reinterpreted from the dwell time, meeting the requirement of velocity and the limitations of accelerations or decelerations. Indeed, the model and algorithm can also apply to other computer-controlled subaperture methods.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Concurrent material-fabrication optimization of metal-matrix laminates under thermo-mechanical loading

NASA Technical Reports Server (NTRS)

Saravanos, D. A.; Morel, M. R.; Chamis, C. C.

1991-01-01

A methodology is developed to tailor fabrication and material parameters of metal-matrix laminates for maximum loading capacity under thermomechanical loads. The stresses during the thermomechanical response are minimized subject to failure constrains and bounds on the laminate properties. The thermomechanical response of the laminate is simulated using nonlinear composite mechanics. Evaluations of the method on a graphite/copper symmetric cross-ply laminate were performed. The cross-ply laminate required different optimum fabrication procedures than a unidirectional composite. Also, the consideration of the thermomechanical cycle had a significant effect on the predicted optimal process.

Globally optimal superconducting magnets part I: minimum stored energy (MSE) current density map.

PubMed

Tieng, Quang M; Vegh, Viktor; Brereton, Ian M

2009-01-01

An optimal current density map is crucial in magnet design to provide the initial values within search spaces in an optimization process for determining the final coil arrangement of the magnet. A strategy for obtaining globally optimal current density maps for the purpose of designing magnets with coaxial cylindrical coils in which the stored energy is minimized within a constrained domain is outlined. The current density maps obtained utilising the proposed method suggests that peak current densities occur around the perimeter of the magnet domain, where the adjacent peaks have alternating current directions for the most compact designs. As the dimensions of the domain are increased, the current density maps yield traditional magnet designs of positive current alone. These unique current density maps are obtained by minimizing the stored magnetic energy cost function and therefore suggest magnet coil designs of minimal system energy. Current density maps are provided for a number of different domain arrangements to illustrate the flexibility of the method and the quality of the achievable designs.

Finite dimensional approximation of a class of constrained nonlinear optimal control problems

NASA Technical Reports Server (NTRS)

Gunzburger, Max D.; Hou, L. S.

1994-01-01

An abstract framework for the analysis and approximation of a class of nonlinear optimal control and optimization problems is constructed. Nonlinearities occur in both the objective functional and in the constraints. The framework includes an abstract nonlinear optimization problem posed on infinite dimensional spaces, and approximate problem posed on finite dimensional spaces, together with a number of hypotheses concerning the two problems. The framework is used to show that optimal solutions exist, to show that Lagrange multipliers may be used to enforce the constraints, to derive an optimality system from which optimal states and controls may be deduced, and to derive existence results and error estimates for solutions of the approximate problem. The abstract framework and the results derived from that framework are then applied to three concrete control or optimization problems and their approximation by finite element methods. The first involves the von Karman plate equations of nonlinear elasticity, the second, the Ginzburg-Landau equations of superconductivity, and the third, the Navier-Stokes equations for incompressible, viscous flows.

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

PubMed

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimization of Low-Thrust Spiral Trajectories by Collocation

NASA Technical Reports Server (NTRS)

Falck, Robert D.; Dankanich, John W.

2012-01-01

As NASA examines potential missions in the post space shuttle era, there has been a renewed interest in low-thrust electric propulsion for both crewed and uncrewed missions. While much progress has been made in the field of software for the optimization of low-thrust trajectories, many of the tools utilize higher-fidelity methods which, while excellent, result in extremely high run-times and poor convergence when dealing with planetocentric spiraling trajectories deep within a gravity well. Conversely, faster tools like SEPSPOT provide a reasonable solution but typically fail to account for other forces such as third-body gravitation, aerodynamic drag, solar radiation pressure. SEPSPOT is further constrained by its solution method, which may require a very good guess to yield a converged optimal solution. Here the authors have developed an approach using collocation intended to provide solution times comparable to those given by SEPSPOT while allowing for greater robustness and extensible force models.

Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

NASA Astrophysics Data System (ADS)

Zaouche, Abdelouahib; Dayoub, Iyad; Rouvaen, Jean Michel; Tatkeu, Charles

2008-12-01

We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR) strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA) are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

On advanced configuration enhance adaptive system optimization

NASA Astrophysics Data System (ADS)

Liu, Hua; Ding, Quanxin; Wang, Helong; Guo, Chunjie; Chen, Hongliang; Zhou, Liwei

2017-10-01

For aim to find an effective method to structure to enhance these adaptive system with some complex function and look forward to establish an universally applicable solution in prototype and optimization. As the most attractive component in adaptive system, wave front corrector is constrained by some conventional technique and components, such as polarization dependence and narrow working waveband. Advanced configuration based on a polarized beam split can optimized energy splitting method used to overcome these problems effective. With the global algorithm, the bandwidth has been amplified by more than five times as compared with that of traditional ones. Simulation results show that the system can meet the application requirements in MTF and other related criteria. Compared with the conventional design, the system has reduced in volume and weight significantly. Therefore, the determining factors are the prototype selection and the system configuration, Results show their effectiveness.

Automatic Summarization as a Combinatorial Optimization Problem

NASA Astrophysics Data System (ADS)

Hirao, Tsutomu; Suzuki, Jun; Isozaki, Hideki

We derived the oracle summary with the highest ROUGE score that can be achieved by integrating sentence extraction with sentence compression from the reference abstract. The analysis results of the oracle revealed that summarization systems have to assign an appropriate compression rate for each sentence in the document. In accordance with this observation, this paper proposes a summarization method as a combinatorial optimization: selecting the set of sentences that maximize the sum of the sentence scores from the pool which consists of the sentences with various compression rates, subject to length constrains. The score of the sentence is defined by its compression rate, content words and positional information. The parameters for the compression rates and positional information are optimized by minimizing the loss between score of oracles and that of candidates. The results obtained from TSC-2 corpus showed that our method outperformed the previous systems with statistical significance.

Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery

NASA Astrophysics Data System (ADS)

Otake, Y.; Leonard, S.; Reiter, A.; Rajan, P.; Siewerdsen, J. H.; Ishii, M.; Taylor, R. H.; Hager, G. D.

2015-03-01

We present a system for registering the coordinate frame of an endoscope to pre- or intra- operatively acquired CT data based on optimizing the similarity metric between an endoscopic image and an image predicted via rendering of CT. Our method is robust and semi-automatic because it takes account of physical constraints, specifically, collisions between the endoscope and the anatomy, to initialize and constrain the search. The proposed optimization method is based on a stochastic optimization algorithm that evaluates a large number of similarity metric functions in parallel on a graphics processing unit. Images from a cadaver and a patient were used for evaluation. The registration error was 0.83 mm and 1.97 mm for cadaver and patient images respectively. The average registration time for 60 trials was 4.4 seconds. The patient study demonstrated robustness of the proposed algorithm against a moderate anatomical deformation.

Necessary conditions for the optimality of variable rate residual vector quantizers

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Smith, Mark J. T.; Barnes, Christopher F.

1993-01-01

Residual vector quantization (RVQ), or multistage VQ, as it is also called, has recently been shown to be a competitive technique for data compression. The competitive performance of RVQ reported in results from the joint optimization of variable rate encoding and RVQ direct-sum code books. In this paper, necessary conditions for the optimality of variable rate RVQ's are derived, and an iterative descent algorithm based on a Lagrangian formulation is introduced for designing RVQ's having minimum average distortion subject to an entropy constraint. Simulation results for these entropy-constrained RVQ's (EC-RVQ's) are presented for memory less Gaussian, Laplacian, and uniform sources. A Gauss-Markov source is also considered. The performance is superior to that of entropy-constrained scalar quantizers (EC-SQ's) and practical entropy-constrained vector quantizers (EC-VQ's), and is competitive with that of some of the best source coding techniques that have appeared in the literature.

Derivative-free generation and interpolation of convex Pareto optimal IMRT plans

NASA Astrophysics Data System (ADS)

Hoffmann, Aswin L.; Siem, Alex Y. D.; den Hertog, Dick; Kaanders, Johannes H. A. M.; Huizenga, Henk

2006-12-01

In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning.

Density-to-Potential Inversions to Guide Development of Exchange-Correlation Approximations at Finite Temperature

NASA Astrophysics Data System (ADS)

Jensen, Daniel; Wasserman, Adam; Baczewski, Andrew

The construction of approximations to the exchange-correlation potential for warm dense matter (WDM) is a topic of significant recent interest. In this work, we study the inverse problem of Kohn-Sham (KS) DFT as a means of guiding functional design at zero temperature and in WDM. Whereas the forward problem solves the KS equations to produce a density from a specified exchange-correlation potential, the inverse problem seeks to construct the exchange-correlation potential from specified densities. These two problems require different computational methods and convergence criteria despite sharing the same mathematical equations. We present two new inversion methods based on constrained variational and PDE-constrained optimization methods. We adapt these methods to finite temperature calculations to reveal the exchange-correlation potential's temperature dependence in WDM-relevant conditions. The different inversion methods presented are applied to both non-interacting and interacting model systems for comparison. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Security Administration under contract DE-AC04-94.

Energetic Materials Optimization via Constrained Search

DTIC Science & Technology

2015-06-01

steps. 3. Optimization Methodology Our optimization problem is formulated as a constrained maximization: max x∈CCS P (x) s.t. : TED ( x )− 9.75 ≥ 0 SV (x)− 9...0 5− SA(x) ≥ 0, (1) where TED ( x ) is the total energy of detonation (TED) of compound x from the chosen chemical subspace (CCS) of chemical compound...max problem, max x∈CCS min λ∈R3+ P (x)− λTC(x), (2) where C(x) is the vector of constraint violations, i.e., η(9.75 − TED ( x )), η(9 − SV (x)), η(SA(x

Optimization of the Number and Location of Tsunami Stations in a Tsunami Warning System

NASA Astrophysics Data System (ADS)

An, C.; Liu, P. L. F.; Pritchard, M. E.

2014-12-01

Optimizing the number and location of tsunami stations in designing a tsunami warning system is an important and practical problem. It is always desirable to maximize the capability of the data obtained from the stations for constraining the earthquake source parameters, and to minimize the number of stations at the same time. During the 2011 Tohoku tsunami event, 28 coastal gauges and DART buoys in the near-field recorded tsunami waves, providing an opportunity for assessing the effectiveness of those stations in identifying the earthquake source parameters. Assuming a single-plane fault geometry, inversions of tsunami data from combinations of various number (1~28) of stations and locations are conducted and evaluated their effectiveness according to the residues of the inverse method. Results show that the optimized locations of stations depend on the number of stations used. If the stations are optimally located, 2~4 stations are sufficient to constrain the source parameters. Regarding the optimized location, stations must be uniformly spread in all directions, which is not surprising. It is also found that stations within the source region generally give worse constraint of earthquake source than stations farther from source, which is due to the exaggeration of model error in matching large amplitude waves at near-source stations. Quantitative discussions on these findings will be given in the presentation. Applying similar analysis to the Manila Trench based on artificial scenarios of earthquakes and tsunamis, the optimal location of tsunami stations are obtained, which provides guidance of deploying a tsunami warning system in this region.

Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views

DOEpatents

Lee, Heung-Rae

1997-01-01

A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object.

Multimaterial topology optimization of contact problems using phase field regularization

NASA Astrophysics Data System (ADS)

Myśliński, Andrzej

2018-01-01

The numerical method to solve multimaterial topology optimization problems for elastic bodies in unilateral contact with Tresca friction is developed in the paper. The displacement of the elastic body in contact is governed by elliptic equation with inequality boundary conditions. The body is assumed to consists from more than two distinct isotropic elastic materials. The materials distribution function is chosen as the design variable. Since high contact stress appears during the contact phenomenon the aim of the structural optimization problem is to find such topology of the domain occupied by the body that the normal contact stress along the boundary of the body is minimized. The original cost functional is regularized using the multiphase volume constrained Ginzburg-Landau energy functional rather than the perimeter functional. The first order necessary optimality condition is recalled and used to formulate the generalized gradient flow equations of Allen-Cahn type. The optimal topology is obtained as the steady state of the phase transition governed by the generalized Allen-Cahn equation. As the interface width parameter tends to zero the transition of the phase field model to the level set model is studied. The optimization problem is solved numerically using the operator splitting approach combined with the projection gradient method. Numerical examples confirming the applicability of the proposed method are provided and discussed.

Constraint Optimization Problem For The Cutting Of A Cobalt Chrome Refractory Material

NASA Astrophysics Data System (ADS)

Lebaal, Nadhir; Schlegel, Daniel; Folea, Milena

2011-05-01

This paper shows a complete approach to solve a given problem, from the experimentation to the optimization of different cutting parameters. In response to an industrial problem of slotting FSX 414, a Cobalt-based refractory material, we have implemented a design of experiment to determine the most influent parameters on the tool life, the surface roughness and the cutting forces. After theses trials, an optimization approach has been implemented to find the lowest manufacturing cost while respecting the roughness constraints and cutting force limitation constraints. The optimization approach is based on the Response Surface Method (RSM) using the Sequential Quadratic programming algorithm (SQP) for a constrained problem. To avoid a local optimum and to obtain an accurate solution at low cost, an efficient strategy, which allows improving the RSM accuracy in the vicinity of the global optimum, is presented. With these models and these trials, we could apply and compare our optimization methods in order to get the lowest cost for the best quality, i.e. a satisfying surface roughness and limited cutting forces.

Maximum margin multiple instance clustering with applications to image and text clustering.

PubMed

Zhang, Dan; Wang, Fei; Si, Luo; Li, Tao

2011-05-01

In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

An adequacy-constrained integrated planning method for effective accommodation of DG and electric vehicles in smart distribution systems

NASA Astrophysics Data System (ADS)

Tan, Zhukui; Xie, Baiming; Zhao, Yuanliang; Dou, Jinyue; Yan, Tong; Liu, Bin; Zeng, Ming

2018-06-01

This paper presents a new integrated planning framework for effective accommodating electric vehicles in smart distribution systems (SDS). The proposed method incorporates various investment options available for the utility collectively, including distributed generation (DG), capacitors and network reinforcement. Using a back-propagation algorithm combined with cost-benefit analysis, the optimal network upgrade plan, allocation and sizing of the selected components are determined, with the purpose of minimizing the total system capital and operating costs of DG and EV accommodation. Furthermore, a new iterative reliability test method is proposed. It can check the optimization results by subsequently simulating the reliability level of the planning scheme, and modify the generation reserve margin to guarantee acceptable adequacy levels for each year of the planning horizon. Numerical results based on a 32-bus distribution system verify the effectiveness of the proposed method.

Cosmological parameter estimation using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Ghost artifact cancellation using phased array processing.

PubMed

Kellman, P; McVeigh, E R

2001-08-01

In this article, a method for phased array combining is formulated which may be used to cancel ghosts caused by a variety of distortion mechanisms, including space variant distortions such as local flow or off-resonance. This method is based on a constrained optimization, which optimizes SNR subject to the constraint of nulling ghost artifacts at known locations. The resultant technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation it is applied to full field-of-view (FOV) images. The method is applied to multishot EPI with noninterleaved phase encode acquisition. A number of benefits, as compared to the conventional interleaved approach, are reduced distortion due to off-resonance, in-plane flow, and EPI delay misalignment, as well as eliminating the need for echo-shifting. Experimental results demonstrate the cancellation for both phantom as well as cardiac imaging examples.

Ghost Artifact Cancellation Using Phased Array Processing

PubMed Central

Kellman, Peter; McVeigh, Elliot R.

2007-01-01

In this article, a method for phased array combining is formulated which may be used to cancel ghosts caused by a variety of distortion mechanisms, including space variant distortions such as local flow or off-resonance. This method is based on a constrained optimization, which optimizes SNR subject to the constraint of nulling ghost artifacts at known locations. The resultant technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation it is applied to full field-of-view (FOV) images. The method is applied to multishot EPI with noninterleaved phase encode acquisition. A number of benefits, as compared to the conventional interleaved approach, are reduced distortion due to off-resonance, in-plane flow, and EPI delay misalignment, as well as eliminating the need for echo-shifting. Experimental results demonstrate the cancellation for both phantom as well as cardiac imaging examples. PMID:11477638

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

PubMed

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Material Distribution Optimization for the Shell Aircraft Composite Structure

NASA Astrophysics Data System (ADS)

Shevtsov, S.; Zhilyaev, I.; Oganesyan, P.; Axenov, V.

2016-09-01

One of the main goal in aircraft structures designing isweight decreasing and stiffness increasing. Composite structures recently became popular in aircraft because of their mechanical properties and wide range of optimization possibilities.Weight distribution and lay-up are keys to creating lightweight stiff strictures. In this paperwe discuss optimization of specific structure that undergoes the non-uniform air pressure at the different flight conditions and reduce a level of noise caused by the airflowinduced vibrations at the constrained weight of the part. Initial model was created with CAD tool Siemens NX, finite element analysis and post processing were performed with COMSOL Multiphysicsr and MATLABr. Numerical solutions of the Reynolds averaged Navier-Stokes (RANS) equations supplemented by k-w turbulence model provide the spatial distributions of air pressure applied to the shell surface. At the formulation of optimization problem the global strain energy calculated within the optimized shell was assumed as the objective. Wall thickness has been changed using parametric approach by an initiation of auxiliary sphere with varied radius and coordinates of the center, which were the design variables. To avoid a local stress concentration, wall thickness increment was defined as smooth function on the shell surface dependent of auxiliary sphere position and size. Our study consists of multiple steps: CAD/CAE transformation of the model, determining wind pressure for different flow angles, optimizing wall thickness distribution for specific flow angles, designing a lay-up for optimal material distribution. The studied structure was improved in terms of maximum and average strain energy at the constrained expense ofweight growth. Developed methods and tools can be applied to wide range of shell-like structures made of multilayered quasi-isotropic laminates.

Mixed Integer Programming and Heuristic Scheduling for Space Communication

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2013-01-01

Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.

Optimization-based additive decomposition of weakly coercive problems with applications

DOE PAGES

Bochev, Pavel B.; Ridzal, Denis

2016-01-27

In this study, we present an abstract mathematical framework for an optimization-based additive decomposition of a large class of variational problems into a collection of concurrent subproblems. The framework replaces a given monolithic problem by an equivalent constrained optimization formulation in which the subproblems define the optimization constraints and the objective is to minimize the mismatch between their solutions. The significance of this reformulation stems from the fact that one can solve the resulting optimality system by an iterative process involving only solutions of the subproblems. Consequently, assuming that stable numerical methods and efficient solvers are available for every subproblem,more » our reformulation leads to robust and efficient numerical algorithms for a given monolithic problem by breaking it into subproblems that can be handled more easily. An application of the framework to the Oseen equations illustrates its potential.« less

Galaxy Redshifts from Discrete Optimization of Correlation Functions

NASA Astrophysics Data System (ADS)

Lee, Benjamin C. G.; Budavári, Tamás; Basu, Amitabh; Rahman, Mubdi

2016-12-01

We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming (ILP) are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of ILP to astronomical analysis.

A Sequential Linear Quadratic Approach for Constrained Nonlinear Optimal Control with Adaptive Time Discretization and Application to Higher Elevation Mars Landing Problem

NASA Astrophysics Data System (ADS)

Sandhu, Amit

A sequential quadratic programming method is proposed for solving nonlinear optimal control problems subject to general path constraints including mixed state-control and state only constraints. The proposed algorithm further develops on the approach proposed in [1] with objective to eliminate the use of a high number of time intervals for arriving at an optimal solution. This is done by introducing an adaptive time discretization to allow formation of a desirable control profile without utilizing a lot of intervals. The use of fewer time intervals reduces the computation time considerably. This algorithm is further used in this thesis to solve a trajectory planning problem for higher elevation Mars landing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhao, Changhong; Zamzam, Admed S.

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successivemore » convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.« less

Determining the Optimal Solution for Quadratically Constrained Quadratic Programming (QCQP) on Energy-Saving Generation Dispatch Problem

NASA Astrophysics Data System (ADS)

Lesmana, E.; Chaerani, D.; Khansa, H. N.

2018-03-01

Energy-Saving Generation Dispatch (ESGD) is a scheme made by Chinese Government in attempt to minimize CO2 emission produced by power plant. This scheme is made related to global warming which is primarily caused by too much CO2 in earth’s atmosphere, and while the need of electricity is something absolute, the power plants producing it are mostly thermal-power plant which produced many CO2. Many approach to fulfill this scheme has been made, one of them came through Minimum Cost Flow in which resulted in a Quadratically Constrained Quadratic Programming (QCQP) form. In this paper, ESGD problem with Minimum Cost Flow in QCQP form will be solved using Lagrange’s Multiplier Method

Optimization of the current potential for stellarator coils

NASA Astrophysics Data System (ADS)

Boozer, Allen H.

2000-02-01

Stellarator plasma confinement devices have no continuous symmetries, which makes the design of appropriate coils far more subtle than for axisymmetric devices such as tokamaks. The modern method for designing coils for stellarators was developed by Peter Merkel [P. Merkel, Nucl. Fusion 27, 867 (1987)]. Although his method has yielded a number of successful stellarator designs, Merkel's method has a systematic tendency to give coils with a larger current than that required to produce a stellarator plasma with certain properties. In addition, Merkel's method does not naturally lead to a coil set with the flexibility to produce a number of interesting plasma configurations. The issues of coil efficiency and flexibility are addressed in this paper by a new method of optimizing the current potential, the first step in Merkel's method. The new method also allows the coil design to be based on a freer choice for the plasma-coil separation and to be constrained so space is preserved for plasma access.

Optimization of the current potential for stellarator coils

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boozer, Allen H.; Max-Planck-Institut fuer Plasmaphysik, EURATOM-Association, D-85748 Garching,

2000-02-01

Stellarator plasma confinement devices have no continuous symmetries, which makes the design of appropriate coils far more subtle than for axisymmetric devices such as tokamaks. The modern method for designing coils for stellarators was developed by Peter Merkel [P. Merkel, Nucl. Fusion 27, 867 (1987)]. Although his method has yielded a number of successful stellarator designs, Merkel's method has a systematic tendency to give coils with a larger current than that required to produce a stellarator plasma with certain properties. In addition, Merkel's method does not naturally lead to a coil set with the flexibility to produce a number ofmore » interesting plasma configurations. The issues of coil efficiency and flexibility are addressed in this paper by a new method of optimizing the current potential, the first step in Merkel's method. The new method also allows the coil design to be based on a freer choice for the plasma-coil separation and to be constrained so space is preserved for plasma access. (c) 2000 American Institute of Physics.« less

An inexact chance-constrained programming model for water quality management in Binhai New Area of Tianjin, China.

PubMed

Xie, Y L; Li, Y P; Huang, G H; Li, Y F; Chen, L R

2011-04-15

In this study, an inexact-chance-constrained water quality management (ICC-WQM) model is developed for planning regional environmental management under uncertainty. This method is based on an integration of interval linear programming (ILP) and chance-constrained programming (CCP) techniques. ICC-WQM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. Complexities in environmental management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method is applied to planning chemical-industry development in Binhai New Area of Tianjin, China. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various system-reliability constraints of water environmental capacity of pollutant. Tradeoffs between system benefits and constraint-violation risks can also be tackled. They are helpful for supporting (a) decision of wastewater discharge and government investment, (b) formulation of local policies regarding water consumption, economic development and industry structure, and (c) analysis of interactions among economic benefits, system reliability and pollutant discharges. Copyright © 2011 Elsevier B.V. All rights reserved.

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

PubMed

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

PubMed Central

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Multi-Constraint Multi-Variable Optimization of Source-Driven Nuclear Systems

NASA Astrophysics Data System (ADS)

Watkins, Edward Francis

1995-01-01

A novel approach to the search for optimal designs of source-driven nuclear systems is investigated. Such systems include radiation shields, fusion reactor blankets and various neutron spectrum-shaping assemblies. The novel approach involves the replacement of the steepest-descents optimization algorithm incorporated in the code SWAN by a significantly more general and efficient sequential quadratic programming optimization algorithm provided by the code NPSOL. The resulting SWAN/NPSOL code system can be applied to more general, multi-variable, multi-constraint shield optimization problems. The constraints it accounts for may include simple bounds on variables, linear constraints, and smooth nonlinear constraints. It may also be applied to unconstrained, bound-constrained and linearly constrained optimization. The shield optimization capabilities of the SWAN/NPSOL code system is tested and verified in a variety of optimization problems: dose minimization at constant cost, cost minimization at constant dose, and multiple-nonlinear constraint optimization. The replacement of the optimization part of SWAN with NPSOL is found feasible and leads to a very substantial improvement in the complexity of optimization problems which can be efficiently handled.

Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances.

PubMed

Zhang, Huaguang; Qu, Qiuxia; Xiao, Geyang; Cui, Yang

2018-06-01

Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. Some simulation results are presented to verify the feasibility of the proposed control scheme.

Top-down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS-Chem adjoint model

NASA Astrophysics Data System (ADS)

Wang, Jun; Xu, Xiaoguang; Henze, Daven K.; Zeng, Jing; Ji, Qiang; Tsay, Si-Chee; Huang, Jianping

2012-04-01

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOS-Chem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

Top-down Estimate of Dust Emissions Through Integration of MODIS and MISR Aerosol Retrievals With the Geos-chem Adjoint Model

NASA Technical Reports Server (NTRS)

Wang, Jun; Xu, Xiaoguang; Henze, Daven K.; Zeng, Jing; Ji, Qiang; Tsay, Si-Chee; Huang, Jianping

2012-01-01

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOSChem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

Mathematical Analysis for the Optimization of Wastewater Treatment Systems in Facultative Pond Indicator Organic Matter

NASA Astrophysics Data System (ADS)

Sunarsih; Widowati; Kartono; Sutrisno

2018-02-01

Stabilization ponds are easy to operate and their maintenance is simple. Treatment is carried out naturally and they are recommended in developing countries. The main disadvantage of these systems is large land area they occupy. The aim of this study was to perform an optimization of the wastewater treatment systems in a facultative pond, considering a mathematical analysis of the methodology to determine the model constrains organic matter. Matlab optimization toolbox was used for non linear programming. A facultative pond with the method was designed and then the optimization system was applied. The analyse meet the treated water quality requirements for the discharge to the water bodies. The results show a reduction of hydraulic retention time by 4.83 days, and the efficiency of of wastewater treatment of 84.16 percent.

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Intelligent Tutoring Methods for Optimizing Learning Outcomes with Embedded Training

DTIC Science & Technology

2009-10-01

after action review. Particularly with free - play virtual environments, it is important to constrain the development task for constructing an...evaluation approach. Attempts to model all possible variations of correct performance can be prohibitive in free - play scenarios, and so for such conditions...member R for proper execution during free - play execution. In the first tier, the evaluation must know when it applies, or more specifically, when

Planning of water resources management and pollution control for Heshui River watershed, China: A full credibility-constrained programming approach.

PubMed

Zhang, Y M; Huang, G; Lu, H W; He, Li

2015-08-15

A key issue facing integrated water resources management and water pollution control is to address the vague parametric information. A full credibility-based chance-constrained programming (FCCP) method is thus developed by introducing the new concept of credibility into the modeling framework. FCCP can deal with fuzzy parameters appearing concurrently in the objective and both sides of the constraints of the model, but also provide a credibility level indicating how much confidence one can believe the optimal modeling solutions. The method is applied to Heshui River watershed in the south-central China for demonstration. Results from the case study showed that groundwater would make up for the water shortage in terms of the shrinking surface water and rising water demand, and the optimized total pumpage of groundwater from both alluvial and karst aquifers would exceed 90% of its maximum allowable levels when credibility level is higher than or equal to 0.9. It is also indicated that an increase in credibility level would induce a reduction in cost for surface water acquisition, a rise in cost from groundwater withdrawal, and negligible variation in cost for water pollution control. Copyright © 2015 Elsevier B.V. All rights reserved.

Maximum likelihood identification and optimal input design for identifying aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Stepner, D. E.; Mehra, R. K.

1973-01-01

A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.

Advanced Computational Methods for Security Constrained Financial Transmission Rights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalsi, Karanjit; Elbert, Stephen T.; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) are financial insurance tools to help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, first an innovative mathematical reformulationmore » of the FTR problem is presented which dramatically improves the computational efficiency of optimization problem. After having re-formulated the problem, a novel non-linear dynamic system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on both standard IEEE test systems and large-scale systems using data from the Western Electricity Coordinating Council (WECC). The performance of the NDS is demonstrated to be comparable and in some cases is shown to outperform the widely used CPLEX algorithms. The proposed formulation and NDS based solver is also easily parallelizable enabling further computational improvement.« less

The Image-Optimized Corona; Progress on Using Coronagraph Images to Constrain Coronal Magnetic Field Models

NASA Astrophysics Data System (ADS)

Jones, S. I.; Uritsky, V. M.; Davila, J. M.

2017-12-01

In absence of reliable coronal magnetic field measurements, solar physicists have worked for several decades to develop techniques for extrapolating photospheric magnetic field measurements into the solar corona and/or heliosphere. The products of these efforts tend to be very sensitive to variation in the photospheric measurements, such that the uncertainty in the photospheric measurements introduces significant uncertainty into the coronal and heliospheric models needed to predict such things as solar wind speed, IMF polarity at Earth, and CME propagation. Ultimately, the reason for the sensitivity of the model to the boundary conditions is that the model is trying to extact a great deal of information from a relatively small amout of data. We have published in recent years about a new method we are developing to use morphological information gleaned from coronagraph images to constrain models of the global coronal magnetic field. In our approach, we treat the photospheric measurements as approximations and use an optimization algorithm to iteratively find a global coronal model that best matches both the photospheric measurements and quasi-linear features observed in polarization brightness coronagraph images. Here we will summarize the approach we have developed and present recent progress in optimizing PFSS models based on GONG magnetograms and MLSO K-Cor images.

A practical approach to superresolution

NASA Astrophysics Data System (ADS)

Farsiu, Sina; Elad, Michael; Milanfar, Peyman

2006-01-01

Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD read out noise) and system noise (e.g. motion estimation error). Noting that the motion estimation is often considered a bottleneck in terms of SR performance, we introduce the concept of "constrained motions" for enhancing the quality of super-resolved images. We show that using such constraints will enhance the quality of the motion estimation and therefore results in more accurate reconstruction of the HR images. We also justify some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. We use efficient approximation of the Kalman Filter (KF) and adopt a dynamic point of view to the SR problem. Novel methods for addressing these issues are accompanied by experimental results on real data.

An Efficient Method Coupling Kernel Principal Component Analysis with Adjoint-Based Optimal Control and Its Goal-Oriented Extensions

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Tong, C. H.; Chen, X.

2016-12-01

The representativeness of available data poses a significant fundamental challenge to the quantification of uncertainty in geophysical systems. Furthermore, the successful application of machine learning methods to geophysical problems involving data assimilation is inherently constrained by the extent to which obtainable data represent the problem considered. We show how the adjoint method, coupled with optimization based on methods of machine learning, can facilitate the minimization of an objective function defined on a space of significantly reduced dimension. By considering uncertain parameters as constituting a stochastic process, the Karhunen-Loeve expansion and its nonlinear extensions furnish an optimal basis with respect to which optimization using L-BFGS can be carried out. In particular, we demonstrate that kernel PCA can be coupled with adjoint-based optimal control methods to successfully determine the distribution of material parameter values for problems in the context of channelized deformable media governed by the equations of linear elasticity. Since certain subsets of the original data are characterized by different features, the convergence rate of the method in part depends on, and may be limited by, the observations used to furnish the kernel principal component basis. By determining appropriate weights for realizations of the stochastic random field, then, one may accelerate the convergence of the method. To this end, we present a formulation of Weighted PCA combined with a gradient-based means using automatic differentiation to iteratively re-weight observations concurrent with the determination of an optimal reduced set control variables in the feature space. We demonstrate how improvements in the accuracy and computational efficiency of the weighted linear method can be achieved over existing unweighted kernel methods, and discuss nonlinear extensions of the algorithm.

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed tomore » solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.« less

Scheduling Aircraft Landings under Constrained Position Shifting

NASA Technical Reports Server (NTRS)

Balakrishnan, Hamsa; Chandran, Bala

2006-01-01

Optimal scheduling of airport runway operations can play an important role in improving the safety and efficiency of the National Airspace System (NAS). Methods that compute the optimal landing sequence and landing times of aircraft must accommodate practical issues that affect the implementation of the schedule. One such practical consideration, known as Constrained Position Shifting (CPS), is the restriction that each aircraft must land within a pre-specified number of positions of its place in the First-Come-First-Served (FCFS) sequence. We consider the problem of scheduling landings of aircraft in a CPS environment in order to maximize runway throughput (minimize the completion time of the landing sequence), subject to operational constraints such as FAA-specified minimum inter-arrival spacing restrictions, precedence relationships among aircraft that arise either from airline preferences or air traffic control procedures that prevent overtaking, and time windows (representing possible control actions) during which each aircraft landing can occur. We present a Dynamic Programming-based approach that scales linearly in the number of aircraft, and describe our computational experience with a prototype implementation on realistic data for Denver International Airport.

New method to design stellarator coils without the winding surface

NASA Astrophysics Data System (ADS)

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao; Wan, Yuanxi

2018-01-01

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal ‘winding’ surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code, named flexible optimized coils using space curves (FOCUS), has been developed. Applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.

Newton-based optimization for Kullback-Leibler nonnegative tensor factorizations

DOE PAGES

Plantenga, Todd; Kolda, Tamara G.; Hansen, Samantha

2015-04-30

Tensor factorizations with nonnegativity constraints have found application in analysing data from cyber traffic, social networks, and other areas. We consider application data best described as being generated by a Poisson process (e.g. count data), which leads to sparse tensors that can be modelled by sparse factor matrices. In this paper, we investigate efficient techniques for computing an appropriate canonical polyadic tensor factorization based on the Kullback–Leibler divergence function. We propose novel subproblem solvers within the standard alternating block variable approach. Our new methods exploit structure and reformulate the optimization problem as small independent subproblems. We employ bound-constrained Newton andmore » quasi-Newton methods. Finally, we compare our algorithms against other codes, demonstrating superior speed for high accuracy results and the ability to quickly find sparse solutions.« less

Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints

NASA Technical Reports Server (NTRS)

Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

2015-01-01

Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.

Time Scale Optimization and the Hunt for Astronomical Cycles in Deep Time Strata

NASA Astrophysics Data System (ADS)

Meyers, Stephen R.

2016-04-01

A valuable attribute of astrochronology is the direct link between chronometer and climate change, providing a remarkable opportunity to constrain the evolution of the surficial Earth System. Consequently, the hunt for astronomical cycles in strata has spurred the development of a rich conceptual framework for climatic/oceanographic change, and has allowed exploration of the geologic record with unprecedented temporal resolution. Accompanying these successes, however, has been a persistent skepticism about appropriate astrochronologic testing and circular reasoning: how does one reliably test for astronomical cycles in stratigraphic data, especially when time is poorly constrained? From this perspective, it would seem that the merits and promise of astrochronology (e.g., a geologic time scale measured in ≤400 kyr increments) also serves as its Achilles heel, if the confirmation of such short rhythms defies rigorous statistical testing. To address these statistical challenges in astrochronologic testing, a new approach has been developed that (1) explicitly evaluates time scale uncertainty, (2) is resilient to common problems associated with spectrum confidence level assessment and 'multiple testing', and (3) achieves high statistical power under a wide range of conditions (it can identify astronomical cycles when present in data). Designated TimeOpt (for "time scale optimization"; Meyers 2015), the method employs a probabilistic linear regression model framework to investigate amplitude modulation and frequency ratios (bundling) in stratigraphic data, while simultaneously determining the optimal time scale. This presentation will review the TimeOpt method, and demonstrate how the flexible statistical framework can be further extended to evaluate (and optimize upon) complex sedimentation rate models, enhancing the statistical power of the approach, and addressing the challenge of unsteady sedimentation. Meyers, S. R. (2015), The evaluation of eccentricity-related amplitude modulation and bundling in paleoclimate data: An inverse approach for astrochronologic testing and time scale optimization, Paleoceanography, 30, doi:10.1002/ 2015PA002850.

Automated geometric optimization for robotic HIFU treatment of liver tumors.

PubMed

Williamson, Tom; Everitt, Scott; Chauhan, Sunita

2018-05-01

High intensity focused ultrasound (HIFU) represents a non-invasive method for the destruction of cancerous tissue within the body. Heating of targeted tissue by focused ultrasound transducers results in the creation of ellipsoidal lesions at the target site, the locations of which can have a significant impact on treatment outcomes. Towards this end, this work describes a method for the optimization of lesion positions within arbitrary tumors, with specific anatomical constraints. A force-based optimization framework was extended to the case of arbitrary tumor position and constrained orientation. Analysis of the approximate reachable treatment volume for the specific case of treatment of liver tumors was performed based on four transducer configurations and constraint conditions derived. Evaluation was completed utilizing simplified spherical and ellipsoidal tumor models and randomly generated tumor volumes. The total volume treated, lesion overlap and healthy tissue ablated was evaluated. Two evaluation scenarios were defined and optimized treatment plans assessed. The optimization framework resulted in improvements of up to 10% in tumor volume treated, and reductions of up to 20% in healthy tissue ablated as compared to the standard lesion rastering approach. Generation of optimized plans proved feasible for both sub- and intercostally located tumors. This work describes an optimized method for the planning of lesion positions during HIFU treatment of liver tumors. The approach allows the determination of optimal lesion locations and orientations, and can be applied to arbitrary tumor shapes and sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Emulsion of Chloramphenicol: an Overwhelming Approach for Ocular Delivery.

PubMed

Ashara, Kalpesh C; Shah, Ketan V

2017-03-01

Ophthalmic formulations of chloramphenicol have poor bioavailability of chloramphenicol in the ocular cavity. The present study aimed at exploring the impact of different oil mixtures in the form of emulsion on the permeability of chloramphenicol after ocular application. Selection of oil mixture and ratio of the components was made by an equilibrium solubility method. An emulsifier was chosen according to its emulsification properties. A constrained simplex centroid design was used for the assessment of the emulsion development. Emulsions were evaluated for physicochemical properties; zone of inhibition, in-vitro diffusion and ex-vivo local accumulation of chloramphenicol. Validation of the design using check-point batch and reduced polynomial equations were also developed. Optimization of the emulsion was developed by software Design® expert 6.0.8. Assessment of the osmolarity, ocular irritation, sterility testing and isotonicity of optimized batch were also made. Parker Neem®, olive and peppermint oils were selected as an oil phase in the ratio 63.64:20.2:16.16. PEG-400 was selected as an emulsifier according to a pseudo-ternary phase diagram. Constrained simplex-centroid design was applied in the range of 25-39% water, 55-69% PEG-400, 5-19% optimized oil mixture, and 1% chloramphenicol. Unpaired Student's t-test showed for in-vitro and ex-vivo studies that there was a significant difference between the optimized batch of emulsion and Chloramphenicol eye caps (a commercial product) according to both were equally safe. The optimized batch of an emulsion of chloramphenicol was found to be as safe as and more effective than Chloramphenicol eye caps.

SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, T; Yue, N; Jabbour, S

2016-06-15

Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components thatmore » were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy guidance.« less

Grid generation and adaptation via Monge-Kantorovich optimization in 2D and 3D

NASA Astrophysics Data System (ADS)

Delzanno, Gian Luca; Chacon, Luis; Finn, John M.

2008-11-01

In a recent paper [1], Monge-Kantorovich (MK) optimization was proposed as a method of grid generation/adaptation in two dimensions (2D). The method is based on the minimization of the L2 norm of grid point displacement, constrained to producing a given positive-definite cell volume distribution (equidistribution constraint). The procedure gives rise to the Monge-Amp'ere (MA) equation: a single, non-linear scalar equation with no free-parameters. The MA equation was solved in Ref. [1] with the Jacobian Free Newton-Krylov technique and several challenging test cases were presented in squared domains in 2D. Here, we extend the work of Ref. [1]. We first formulate the MK approach in physical domains with curved boundary elements and in 3D. We then show the results of applying it to these more general cases. We show that MK optimization produces optimal grids in which the constraint is satisfied numerically to truncation error. [1] G.L. Delzanno, L. Chac'on, J.M. Finn, Y. Chung, G. Lapenta, A new, robust equidistribution method for two-dimensional grid generation, submitted to Journal of Computational Physics (2008).

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

Vehicle routing problem with time windows using natural inspired algorithms

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.

2018-03-01

Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.

Constrained optimization of image restoration filters

NASA Technical Reports Server (NTRS)

Riemer, T. E.; Mcgillem, C. D.

1973-01-01

A linear shift-invariant preprocessing technique is described which requires no specific knowledge of the image parameters and which is sufficiently general to allow the effective radius of the composite imaging system to be minimized while constraining other system parameters to remain within specified limits.

Constrained minimization of smooth functions using a genetic algorithm

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Analysis of Formation Flying in Eccentric Orbits Using Linearized Equations of Relative Motion

NASA Technical Reports Server (NTRS)

Lane, Christopher; Axelrad, Penina

2004-01-01

Geometrical methods for formation flying design based on the analytical solution to Hill's equations have been previously developed and used to specify desired relative motions in near circular orbits. By generating relationships between the vehicles that are intuitive, these approaches offer valuable insight into the relative motion and allow for the rapid design of satellite configurations to achieve mission specific requirements, such as vehicle separation at perigee or apogee, minimum separation, or a specific geometrical shape. Furthermore, the results obtained using geometrical approaches can be used to better constrain numerical optimization methods; allowing those methods to converge to optimal satellite configurations faster. This paper presents a set of geometrical relationships for formations in eccentric orbits, where Hill.s equations are not valid, and shows how these relationships can be used to investigate formation designs and how they evolve with time.

Force sensing using 3D displacement measurements in linear elastic bodies

NASA Astrophysics Data System (ADS)

Feng, Xinzeng; Hui, Chung-Yuen

2016-07-01

In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the "optimal" traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green's functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints.

Matching CT and ultrasound data of the liver by landmark constrained image registration

NASA Astrophysics Data System (ADS)

Olesch, Janine; Papenberg, Nils; Lange, Thomas; Conrad, Matthias; Fischer, Bernd

2009-02-01

In navigated liver surgery the key challenge is the registration of pre-operative planing and intra-operative navigation data. Due to the patients individual anatomy the planning is based on segmented, pre-operative CT scans whereas ultrasound captures the actual intra-operative situation. In this paper we derive a novel method based on variational image registration methods and additional given anatomic landmarks. For the first time we embed the landmark information as inequality hard constraints and thereby allowing for inaccurately placed landmarks. The yielding optimization problem allows to ensure the accuracy of the landmark fit by simultaneous intensity based image registration. Following the discretize-then-optimize approach the overall problem is solved by a generalized Gauss-Newton-method. The upcoming linear system is attacked by the MinRes solver. We demonstrate the applicability of the new approach for clinical data which lead to convincing results.

A Grobner Basis Solution for Lightning Ground Flash Fraction Retrieval

NASA Technical Reports Server (NTRS)

Solakiewicz, Richard; Attele, Rohan; Koshak, William

2011-01-01

A Bayesian inversion method was previously introduced for retrieving the fraction of ground flashes in a set of flashes observed from a (low earth orbiting or geostationary) satellite lightning imager. The method employed a constrained mixed exponential distribution model to describe the lightning optical measurements. To obtain the optimum model parameters, a scalar function was minimized by a numerical method. In order to improve this optimization, we introduce a Grobner basis solution to obtain analytic representations of the model parameters that serve as a refined initialization scheme to the numerical optimization. Using the Grobner basis, we show that there are exactly 2 solutions involving the first 3 moments of the (exponentially distributed) data. When the mean of the ground flash optical characteristic (e.g., such as the Maximum Group Area, MGA) is larger than that for cloud flashes, then a unique solution can be obtained.

Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kouri, Drew Philip; Surowiec, Thomas M.

Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is inadequate to simulate the underlying physics without quantifying the uncertainty in unknown or random inputs, boundary and initial conditions, and modeling assumptions. Here in this paper, we introduce a general framework for analyzing risk-averse optimization problems constrained by partial differential equations (PDEs). In particular, we postulate conditions on the random variable objective function as well as the PDE solution that guarantee existence of minimizers. Furthermore, we derive optimality conditions and apply our results to the control of an environmental contaminant. Lastly, we introduce a new riskmore » measure, called the conditional entropic risk, that fuses desirable properties from both the conditional value-at-risk and the entropic risk measures.« less

Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization

DOE PAGES

Kouri, Drew Philip; Surowiec, Thomas M.

2018-06-05

Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is inadequate to simulate the underlying physics without quantifying the uncertainty in unknown or random inputs, boundary and initial conditions, and modeling assumptions. Here in this paper, we introduce a general framework for analyzing risk-averse optimization problems constrained by partial differential equations (PDEs). In particular, we postulate conditions on the random variable objective function as well as the PDE solution that guarantee existence of minimizers. Furthermore, we derive optimality conditions and apply our results to the control of an environmental contaminant. Lastly, we introduce a new riskmore » measure, called the conditional entropic risk, that fuses desirable properties from both the conditional value-at-risk and the entropic risk measures.« less

Augmented Lagrange Hopfield network for solving economic dispatch problem in competitive environment

NASA Astrophysics Data System (ADS)

Vo, Dieu Ngoc; Ongsakul, Weerakorn; Nguyen, Khai Phuc

2012-11-01

This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem in the competitive environment. The proposed ALHN is a continuous Hopfield network with its energy function based on augmented Lagrange function for efficiently dealing with constrained optimization problems. The ALHN method can overcome the drawbacks of the conventional Hopfield network such as local optimum, long computational time, and linear constraints. The proposed method is used for solving the ED problem with two revenue models of revenue based on payment for power delivered and payment for reserve allocated. The proposed ALHN has been tested on two systems of 3 units and 10 units for the two considered revenue models. The obtained results from the proposed methods are compared to those from differential evolution (DE) and particle swarm optimization (PSO) methods. The result comparison has indicated that the proposed method is very efficient for solving the problem. Therefore, the proposed ALHN could be a favorable tool for ED problem in the competitive environment.

Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

PubMed Central

Liu, Xing; Hou, Kun Mean; de Vaulx, Christophe; Xu, Jun; Yang, Jianfeng; Zhou, Haiying; Shi, Hongling; Zhou, Peng

2015-01-01

Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN) nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS) LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes. PMID:25545264

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

NASA Astrophysics Data System (ADS)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization.

PubMed

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

PubMed Central

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. PMID:27127499

A Nonlinear Programming Perspective on Sensitivity Calculations for Systems Governed by State Equations

NASA Technical Reports Server (NTRS)

Lewis, Robert Michael

1997-01-01

This paper discusses the calculation of sensitivities. or derivatives, for optimization problems involving systems governed by differential equations and other state relations. The subject is examined from the point of view of nonlinear programming, beginning with the analytical structure of the first and second derivatives associated with such problems and the relation of these derivatives to implicit differentiation and equality constrained optimization. We also outline an error analysis of the analytical formulae and compare the results with similar results for finite-difference estimates of derivatives. We then attend to an investigation of the nature of the adjoint method and the adjoint equations and their relation to directions of steepest descent. We illustrate the points discussed with an optimization problem in which the variables are the coefficients in a differential operator.

Energy aware swarm optimization with intercluster search for wireless sensor network.

PubMed

Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

2015-01-01

Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views

DOEpatents

Lee, H.R.

1997-11-18

A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object. 5 figs.

Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept

PubMed Central

Wang, Fei; Salous, Sana; Zhou, Jianjiang

2017-01-01

In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme. PMID:29186850

Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept.

PubMed

Shi, Chenguang; Wang, Fei; Salous, Sana; Zhou, Jianjiang

2017-11-25

In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme.

Mixed-Strategy Chance Constrained Optimal Control

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J.

2013-01-01

This paper presents a novel chance constrained optimal control (CCOC) algorithm that chooses a control action probabilistically. A CCOC problem is to find a control input that minimizes the expected cost while guaranteeing that the probability of violating a set of constraints is below a user-specified threshold. We show that a probabilistic control approach, which we refer to as a mixed control strategy, enables us to obtain a cost that is better than what deterministic control strategies can achieve when the CCOC problem is nonconvex. The resulting mixed-strategy CCOC problem turns out to be a convexification of the original nonconvex CCOC problem. Furthermore, we also show that a mixed control strategy only needs to "mix" up to two deterministic control actions in order to achieve optimality. Building upon an iterative dual optimization, the proposed algorithm quickly converges to the optimal mixed control strategy with a user-specified tolerance.

A sampling and classification item selection approach with content balancing.

PubMed

Chen, Pei-Hua

2015-03-01

Existing automated test assembly methods typically employ constrained combinatorial optimization. Constructing forms sequentially based on an optimization approach usually results in unparallel forms and requires heuristic modifications. Methods based on a random search approach have the major advantage of producing parallel forms sequentially without further adjustment. This study incorporated a flexible content-balancing element into the statistical perspective item selection method of the cell-only method (Chen et al. in Educational and Psychological Measurement, 72(6), 933-953, 2012). The new method was compared with a sequential interitem distance weighted deviation model (IID WDM) (Swanson & Stocking in Applied Psychological Measurement, 17(2), 151-166, 1993), a simultaneous IID WDM, and a big-shadow-test mixed integer programming (BST MIP) method to construct multiple parallel forms based on matching a reference form item-by-item. The results showed that the cell-only method with content balancing and the sequential and simultaneous versions of IID WDM yielded results comparable to those obtained using the BST MIP method. The cell-only method with content balancing is computationally less intensive than the sequential and simultaneous versions of IID WDM.

Reliability Constrained Priority Load Shedding for Aerospace Power System Automation

NASA Technical Reports Server (NTRS)

Momoh, James A.; Zhu, Jizhong; Kaddah, Sahar S.; Dolce, James L. (Technical Monitor)

2000-01-01

The need for improving load shedding on board the space station is one of the goals of aerospace power system automation. To accelerate the optimum load-shedding functions, several constraints must be involved. These constraints include congestion margin determined by weighted probability contingency, component/system reliability index, generation rescheduling. The impact of different faults and indices for computing reliability were defined before optimization. The optimum load schedule is done based on priority, value and location of loads. An optimization strategy capable of handling discrete decision making, such as Everett optimization, is proposed. We extended Everett method to handle expected congestion margin and reliability index as constraints. To make it effective for real time load dispatch process, a rule-based scheme is presented in the optimization method. It assists in selecting which feeder load to be shed, the location of the load, the value, priority of the load and cost benefit analysis of the load profile is included in the scheme. The scheme is tested using a benchmark NASA system consisting of generators, loads and network.

Optimal Shakedown of the Thin-Wall Metal Structures Under Strength and Stiffness Constraints

NASA Astrophysics Data System (ADS)

Alawdin, Piotr; Liepa, Liudas

2017-06-01

Classical optimization problems of metal structures confined mainly with 1st class cross-sections. But in practice it is common to use the cross-sections of higher classes. In this paper, a new mathematical model for described shakedown optimization problem for metal structures, which elements are designed from 1st to 4th class cross-sections, under variable quasi-static loads is presented. The features of limited plastic redistribution of forces in the structure with thin-walled elements there are taken into account. Authors assume the elastic-plastic flexural buckling in one plane without lateral torsional buckling behavior of members. Design formulae for Methods 1 and 2 for members are analyzed. Structures stiffness constrains are also incorporated in order to satisfy the limit serviceability state requirements. With the help of mathematical programming theory and extreme principles the structure optimization algorithm is developed and justified with the numerical experiment for the metal plane frames.

Real-Time Optimization for use in a Control Allocation System to Recover from Pilot Induced Oscillations

NASA Technical Reports Server (NTRS)

Leonard, Michael W.

2013-01-01

Integration of the Control Allocation technique to recover from Pilot Induced Oscillations (CAPIO) System into the control system of a Short Takeoff and Landing Mobility Concept Vehicle simulation presents a challenge because the CAPIO formulation requires that constrained optimization problems be solved at the controller operating frequency. We present a solution that utilizes a modified version of the well-known L-BFGS-B solver. Despite the iterative nature of the solver, the method is seen to converge in real time with sufficient reliability to support three weeks of piloted runs at the NASA Ames Vertical Motion Simulator (VMS) facility. The results of the optimization are seen to be excellent in the vast majority of real-time frames. Deficiencies in the quality of the results in some frames are shown to be improvable with simple termination criteria adjustments, though more real-time optimization iterations would be required.

Sequential quadratic programming-based fast path planning algorithm subject to no-fly zone constraints

NASA Astrophysics Data System (ADS)

Liu, Wei; Ma, Shunjian; Sun, Mingwei; Yi, Haidong; Wang, Zenghui; Chen, Zengqiang

2016-08-01

Path planning plays an important role in aircraft guided systems. Multiple no-fly zones in the flight area make path planning a constrained nonlinear optimization problem. It is necessary to obtain a feasible optimal solution in real time. In this article, the flight path is specified to be composed of alternate line segments and circular arcs, in order to reformulate the problem into a static optimization one in terms of the waypoints. For the commonly used circular and polygonal no-fly zones, geometric conditions are established to determine whether or not the path intersects with them, and these can be readily programmed. Then, the original problem is transformed into a form that can be solved by the sequential quadratic programming method. The solution can be obtained quickly using the Sparse Nonlinear OPTimizer (SNOPT) package. Mathematical simulations are used to verify the effectiveness and rapidity of the proposed algorithm.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Optimization of an exchange-correlation density functional for water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fritz, Michelle; Fernández-Serra, Marivi; Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York 11794-3800

2016-06-14

We describe a method, that we call data projection onto parameter space (DPPS), to optimize an energy functional of the electron density, so that it reproduces a dataset of experimental magnitudes. Our scheme, based on Bayes theorem, constrains the optimized functional not to depart unphysically from existing ab initio functionals. The resulting functional maximizes the probability of being the “correct” parameterization of a given functional form, in the sense of Bayes theory. The application of DPPS to water sheds new light on why density functional theory has performed rather poorly for liquid water, on what improvements are needed, and onmore » the intrinsic limitations of the generalized gradient approximation to electron exchange and correlation. Finally, we present tests of our water-optimized functional, that we call vdW-DF-w, showing that it performs very well for a variety of condensed water systems.« less

An interior-point method for total variation regularized positron emission tomography image reconstruction

NASA Astrophysics Data System (ADS)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Optimization of contrast resolution by genetic algorithm in ultrasound tissue harmonic imaging.

PubMed

Ménigot, Sébastien; Girault, Jean-Marc

2016-09-01

The development of ultrasound imaging techniques such as pulse inversion has improved tissue harmonic imaging. Nevertheless, no recommendation has been made to date for the design of the waveform transmitted through the medium being explored. Our aim was therefore to find automatically the optimal "imaging" wave which maximized the contrast resolution without a priori information. To overcome assumption regarding the waveform, a genetic algorithm investigated the medium thanks to the transmission of stochastic "explorer" waves. Moreover, these stochastic signals could be constrained by the type of generator available (bipolar or arbitrary). To implement it, we changed the current pulse inversion imaging system by including feedback. Thus the method optimized the contrast resolution by adaptively selecting the samples of the excitation. In simulation, we benchmarked the contrast effectiveness of the best found transmitted stochastic commands and the usual fixed-frequency command. The optimization method converged quickly after around 300 iterations in the same optimal area. These results were confirmed experimentally. In the experimental case, the contrast resolution measured on a radiofrequency line could be improved by 6% with a bipolar generator and it could still increase by 15% with an arbitrary waveform generator. Copyright © 2016 Elsevier B.V. All rights reserved.

Genetic algorithm-based multi-objective optimal absorber system for three-dimensional seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Li, Hongnan; Song, Gangbing; Huo, Linsheng

2009-03-01

The problem of optimizing an absorber system for three-dimensional seismic structures is addressed. The objective is to determine the number and position of absorbers to minimize the coupling effects of translation-torsion of structures at minimum cost. A procedure for a multi-objective optimization problem is developed by integrating a dominance-based selection operator and a dominance-based penalty function method. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. The technique guarantees the better performing individual winning its competition, provides a slight selection pressure toward individuals and maintains diversity in the population. Moreover, due to the evaluation for individuals in each generation being finished in one run, less computational effort is taken. Penalty function methods are generally used to transform a constrained optimization problem into an unconstrained one. The dominance-based penalty function contains necessary information on non-dominated character and infeasible position of an individual, essential for success in seeking a Pareto optimal set. The proposed approach is used to obtain a set of non-dominated designs for a six-storey three-dimensional building with shape memory alloy dampers subjected to earthquake.

Recovery of sparse translation-invariant signals with continuous basis pursuit

PubMed Central

Ekanadham, Chaitanya; Tranchina, Daniel; Simoncelli, Eero

2013-01-01

We consider the problem of decomposing a signal into a linear combination of features, each a continuously translated version of one of a small set of elementary features. Although these constituents are drawn from a continuous family, most current signal decomposition methods rely on a finite dictionary of discrete examples selected from this family (e.g., shifted copies of a set of basic waveforms), and apply sparse optimization methods to select and solve for the relevant coefficients. Here, we generate a dictionary that includes auxiliary interpolation functions that approximate translates of features via adjustment of their coefficients. We formulate a constrained convex optimization problem, in which the full set of dictionary coefficients represents a linear approximation of the signal, the auxiliary coefficients are constrained so as to only represent translated features, and sparsity is imposed on the primary coefficients using an L1 penalty. The basis pursuit denoising (BP) method may be seen as a special case, in which the auxiliary interpolation functions are omitted, and we thus refer to our methodology as continuous basis pursuit (CBP). We develop two implementations of CBP for a one-dimensional translation-invariant source, one using a first-order Taylor approximation, and another using a form of trigonometric spline. We examine the tradeoff between sparsity and signal reconstruction accuracy in these methods, demonstrating empirically that trigonometric CBP substantially outperforms Taylor CBP, which in turn offers substantial gains over ordinary BP. In addition, the CBP bases can generally achieve equally good or better approximations with much coarser sampling than BP, leading to a reduction in dictionary dimensionality. PMID:24352562

Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

NASA Astrophysics Data System (ADS)

Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

2018-06-01

Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

Optimization of natural frequencies of a slender beam shaped in a linear combination of its mode shapes

NASA Astrophysics Data System (ADS)

Silva, Guilherme Augusto Lopes da; Nicoletti, Rodrigo

2017-06-01

This work focuses on the placement of natural frequencies of beams to desired frequency regions. More specifically, we investigate the effects of combining mode shapes to shape a beam to change its natural frequencies, both numerically and experimentally. First, we present a parametric analysis of a shaped beam and we analyze the resultant effects for different boundary conditions and mode shapes. Second, we present an optimization procedure to find the optimum shape of the beam for desired natural frequencies. In this case, we adopt the Nelder-Mead simplex search method, which allows a broad search of the optimum shape in the solution domain. Finally, the obtained results are verified experimentally for a clamped-clamped beam in three different optimization runs. Results show that the method is effective in placing natural frequencies at desired values (experimental results lie within a 10% error to the expected theoretical ones). However, the beam must be axially constrained to have the natural frequencies changed.

Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Walter, Ulrich

2015-07-01

This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize themore » system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.« less

Solution techniques for transient stability-constrained optimal power flow – Part II

DOE PAGES

Geng, Guangchao; Abhyankar, Shrirang; Wang, Xiaoyu; ...

2017-06-28

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

Solution techniques for transient stability-constrained optimal power flow – Part II

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Guangchao; Abhyankar, Shrirang; Wang, Xiaoyu

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

Necessary optimality conditions for infinite dimensional state constrained control problems

NASA Astrophysics Data System (ADS)

Frankowska, H.; Marchini, E. M.; Mazzola, M.

2018-06-01

This paper is concerned with first order necessary optimality conditions for state constrained control problems in separable Banach spaces. Assuming inward pointing conditions on the constraint, we give a simple proof of Pontryagin maximum principle, relying on infinite dimensional neighboring feasible trajectories theorems proved in [20]. Further, we provide sufficient conditions guaranteeing normality of the maximum principle. We work in the abstract semigroup setting, but nevertheless we apply our results to several concrete models involving controlled PDEs. Pointwise state constraints (as positivity of the solutions) are allowed.

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.

PubMed

Fiedler, Anna; Raeth, Sebastian; Theis, Fabian J; Hausser, Angelika; Hasenauer, Jan

2016-08-22

Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the analysis of the dataset for Raf/MEK/ERK signaling provides novel biological insights regarding the existence of feedback regulation. Many optimization problems considered in systems and computational biology are subject to steady-state constraints. While most optimization methods have convergence problems if these steady-state constraints are highly nonlinear, the methods presented recover the convergence properties of optimizers which can exploit an analytical expression for the parameter-dependent steady state. This renders them an excellent alternative to methods which are currently employed in systems and computational biology.

Intelligent Sampling of Hazardous Particle Populations in Resource-Constrained Environments

NASA Astrophysics Data System (ADS)

McCollough, J. P.; Quinn, J. M.; Starks, M. J.; Johnston, W. R.

2017-10-01

Sampling of anomaly-causing space environment drivers is necessary for both real-time operations and satellite design efforts, and optimizing measurement sampling helps minimize resource demands. Relating these measurements to spacecraft anomalies requires the ability to resolve spatial and temporal variability in the energetic charged particle hazard of interest. Here we describe a method for sampling particle fluxes informed by magnetospheric phenomenology so that, along a given trajectory, the variations from both temporal dynamics and spatial structure are adequately captured while minimizing oversampling. We describe the coordinates, sampling method, and specific regions and parameters employed. We compare resulting sampling cadences with data from spacecraft spanning the regions of interest during a geomagnetically active period, showing that the algorithm retains the gross features necessary to characterize environmental impacts on space systems in diverse orbital regimes while greatly reducing the amount of sampling required. This enables sufficient environmental specification within a resource-constrained context, such as limited telemetry bandwidth, processing requirements, and timeliness.

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints.

PubMed

Tchamna, Rodrigue; Lee, Moonyong

2018-01-01

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Maximum Constrained Directivity of Oversteered End-Fire Sensor Arrays

PubMed Central

Trucco, Andrea; Traverso, Federico; Crocco, Marco

2015-01-01

For linear arrays with fixed steering and an inter-element spacing smaller than one half of the wavelength, end-fire steering of a data-independent beamformer offers better directivity than broadside steering. The introduction of a lower bound on the white noise gain ensures the necessary robustness against random array errors and sensor mismatches. However, the optimum broadside performance can be obtained using a simple processing architecture, whereas the optimum end-fire performance requires a more complicated system (because complex weight coefficients are needed). In this paper, we reconsider the oversteering technique as a possible way to simplify the processing architecture of equally spaced end-fire arrays. We propose a method for computing the amount of oversteering and the related real-valued weight vector that allows the constrained directivity to be maximized for a given inter-element spacing. Moreover, we verify that the maximized oversteering performance is very close to the optimum end-fire performance. We conclude that optimized oversteering is a viable method for designing end-fire arrays that have better constrained directivity than broadside arrays but with a similar implementation complexity. A numerical simulation is used to perform a statistical analysis, which confirms that the maximized oversteering performance is robust against sensor mismatches. PMID:26066987

An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems.

PubMed

Bravi, Luca; Piccialli, Veronica; Sciandrone, Marco

2017-04-01

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.

Pareto-optimal estimates that constrain mean California precipitation change

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.

PubMed

Liu, Yi; Shangguan, Hong; Zhang, Quan; Zhu, Hongqing; Shu, Huazhong; Gui, Zhiguo

2015-05-01

It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method. Copyright © 2015 Elsevier Ltd. All rights reserved.

PSQP: Puzzle Solving by Quadratic Programming.

PubMed

Andalo, Fernanda A; Taubin, Gabriel; Goldenstein, Siome

2017-02-01

In this article we present the first effective method based on global optimization for the reconstruction of image puzzles comprising rectangle pieces-Puzzle Solving by Quadratic Programming (PSQP). The proposed novel mathematical formulation reduces the problem to the maximization of a constrained quadratic function, which is solved via a gradient ascent approach. The proposed method is deterministic and can deal with arbitrary identical rectangular pieces. We provide experimental results showing its effectiveness when compared to state-of-the-art approaches. Although the method was developed to solve image puzzles, we also show how to apply it to the reconstruction of simulated strip-shredded documents, broadening its applicability.

Time and frequency constrained sonar signal design for optimal detection of elastic objects.

PubMed

Hamschin, Brandon; Loughlin, Patrick J

2013-04-01

In this paper, the task of model-based transmit signal design for optimizing detection is considered. Building on past work that designs the spectral magnitude for optimizing detection, two methods for synthesizing minimum duration signals with this spectral magnitude are developed. The methods are applied to the design of signals that are optimal for detecting elastic objects in the presence of additive noise and self-noise. Elastic objects are modeled as linear time-invariant systems with known impulse responses, while additive noise (e.g., ocean noise or receiver noise) and acoustic self-noise (e.g., reverberation or clutter) are modeled as stationary Gaussian random processes with known power spectral densities. The first approach finds the waveform that preserves the optimal spectral magnitude while achieving the minimum temporal duration. The second approach yields a finite-length time-domain sequence by maximizing temporal energy concentration, subject to the constraint that the spectral magnitude is close (in a least-squares sense) to the optimal spectral magnitude. The two approaches are then connected analytically, showing the former is a limiting case of the latter. Simulation examples that illustrate the theory are accompanied by discussions that address practical applicability and how one might satisfy the need for target and environmental models in the real-world.

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

New method to design stellarator coils without the winding surface

DOE PAGES

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao; ...

2017-11-06

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal 'winding' surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code,more » named flexible optimized coils using space curves (FOCUS), has been developed. Furthermore, applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.« less

New method to design stellarator coils without the winding surface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Caoxiang; Hudson, Stuart R.; Song, Yuntao

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal 'winding' surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code,more » named flexible optimized coils using space curves (FOCUS), has been developed. Furthermore, applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.« less

Applications of Evolutionary Technology to Manufacturing and Logistics Systems : State-of-the Art Survey

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin

Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems. In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.

Variational Trajectory Optimization Tool Set: Technical description and user's manual

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Queen, Eric M.; Cavanaugh, Michael D.; Wetzel, Todd A.; Moerder, Daniel D.

1993-01-01

The algorithms that comprise the Variational Trajectory Optimization Tool Set (VTOTS) package are briefly described. The VTOTS is a software package for solving nonlinear constrained optimal control problems from a wide range of engineering and scientific disciplines. The VTOTS package was specifically designed to minimize the amount of user programming; in fact, for problems that may be expressed in terms of analytical functions, the user needs only to define the problem in terms of symbolic variables. This version of the VTOTS does not support tabular data; thus, problems must be expressed in terms of analytical functions. The VTOTS package consists of two methods for solving nonlinear optimal control problems: a time-domain finite-element algorithm and a multiple shooting algorithm. These two algorithms, under the VTOTS package, may be run independently or jointly. The finite-element algorithm generates approximate solutions, whereas the shooting algorithm provides a more accurate solution to the optimization problem. A user's manual, some examples with results, and a brief description of the individual subroutines are included.

Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method

DOE PAGES

Huang, Kuo -Ling; Mehrotra, Sanjay

2016-11-08

We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadraticmore » programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. In addition, we also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).« less

A closed-form trim solution yielding minimum trim drag for airplanes with multiple longitudinal-control effectors

NASA Technical Reports Server (NTRS)

Goodrich, Kenneth H.; Sliwa, Steven M.; Lallman, Frederick J.

1989-01-01

Airplane designs are currently being proposed with a multitude of lifting and control devices. Because of the redundancy in ways to generate moments and forces, there are a variety of strategies for trimming each airplane. A linear optimum trim solution (LOTS) is derived using a Lagrange formulation. LOTS enables the rapid calculation of the longitudinal load distribution resulting in the minimum trim drag in level, steady-state flight for airplanes with a mixture of three or more aerodynamic surfaces and propulsive control effectors. Comparisons of the trim drags obtained using LOTS, a direct constrained optimization method, and several ad hoc methods are presented for vortex-lattice representations of a three-surface airplane and two-surface airplane with thrust vectoring. These comparisons show that LOTS accurately predicts the results obtained from the nonlinear optimization and that the optimum methods result in trim drag reductions of up to 80 percent compared to the ad hoc methods.

Advanced Imaging Methods for Long-Baseline Optical Interferometry

NASA Astrophysics Data System (ADS)

Le Besnerais, G.; Lacour, S.; Mugnier, L. M.; Thiebaut, E.; Perrin, G.; Meimon, S.

2008-11-01

We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, Mira and Wisard, representative of the two generic approaches for dealing with the missing phase information. Mira is based on an implicit approach and a direct optimization of a Bayesian criterion while Wisard adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called ldquosoft support constraintrdquo that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging.

Successive equimarginal approach for optimal design of a pump and treat system

NASA Astrophysics Data System (ADS)

Guo, Xiaoniu; Zhang, Chuan-Mian; Borthwick, John C.

2007-08-01

An economic concept-based optimization method is developed for groundwater remediation design. Design of a pump and treat (P&T) system is viewed as a resource allocation problem constrained by specified cleanup criteria. An optimal allocation of resources requires that the equimarginal principle, a fundamental economic principle, must hold. The proposed method is named successive equimarginal approach (SEA), which continuously shifts a pumping rate from a less effective well to a more effective one until equal marginal productivity for all units is reached. Through the successive process, the solution evenly approaches the multiple inequality constraints that represent the specified cleanup criteria in space and in time. The goal is to design an equal protection system so that the distributed contaminant plumes can be equally contained without bypass and overprotection is minimized. SEA is a hybrid of the gradient-based method and the deterministic heuristics-based method, which allows flexibility in dealing with multiple inequality constraints without using a penalty function and in balancing computational efficiency with robustness. This method was applied to design a large-scale P&T system for containment of multiple plumes at the former Blaine Naval Ammunition Depot (NAD) site, near Hastings, Nebraska. To evaluate this method, the SEA results were also compared with those using genetic algorithms.

Constrained variational calculus for higher order classical field theories

NASA Astrophysics Data System (ADS)

Campos, Cédric M.; de León, Manuel; Martín de Diego, David

2010-11-01

We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

Active flutter suppression using optical output feedback digital controllers

NASA Technical Reports Server (NTRS)

1982-01-01

A method for synthesizing digital active flutter suppression controllers using the concept of optimal output feedback is presented. A convergent algorithm is employed to determine constrained control law parameters that minimize an infinite time discrete quadratic performance index. Low order compensator dynamics are included in the control law and the compensator parameters are computed along with the output feedback gain as part of the optimization process. An input noise adjustment procedure is used to improve the stability margins of the digital active flutter controller. Sample rate variation, prefilter pole variation, control structure variation and gain scheduling are discussed. A digital control law which accommodates computation delay can stabilize the wing with reasonable rms performance and adequate stability margins.

Augmented Lagrange Programming Neural Network for Localization Using Time-Difference-of-Arrival Measurements.

PubMed

Han, Zifa; Leung, Chi Sing; So, Hing Cheung; Constantinides, Anthony George

2017-08-15

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

An analytical optimization model for infrared image enhancement via local context

NASA Astrophysics Data System (ADS)

Xu, Yongjian; Liang, Kun; Xiong, Yiru; Wang, Hui

2017-12-01

The requirement for high-quality infrared images is constantly increasing in both military and civilian areas, and it is always associated with little distortion and appropriate contrast, while infrared images commonly have some shortcomings such as low contrast. In this paper, we propose a novel infrared image histogram enhancement algorithm based on local context. By constraining the enhanced image to have high local contrast, a regularized analytical optimization model is proposed to enhance infrared images. The local contrast is determined by evaluating whether two intensities are neighbors and calculating their differences. The comparison on 8-bit images shows that the proposed method can enhance the infrared images with more details and lower noise.

Adaptive Multi-Agent Systems for Constrained Optimization

NASA Technical Reports Server (NTRS)

Macready, William; Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

A Near-Optimal Distributed QoS Constrained Routing Algorithm for Multichannel Wireless Sensor Networks

PubMed Central

Lin, Frank Yeong-Sung; Hsiao, Chiu-Han; Yen, Hong-Hsu; Hsieh, Yu-Jen

2013-01-01

One of the important applications in Wireless Sensor Networks (WSNs) is video surveillance that includes the tasks of video data processing and transmission. Processing and transmission of image and video data in WSNs has attracted a lot of attention in recent years. This is known as Wireless Visual Sensor Networks (WVSNs). WVSNs are distributed intelligent systems for collecting image or video data with unique performance, complexity, and quality of service challenges. WVSNs consist of a large number of battery-powered and resource constrained camera nodes. End-to-end delay is a very important Quality of Service (QoS) metric for video surveillance application in WVSNs. How to meet the stringent delay QoS in resource constrained WVSNs is a challenging issue that requires novel distributed and collaborative routing strategies. This paper proposes a Near-Optimal Distributed QoS Constrained (NODQC) routing algorithm to achieve an end-to-end route with lower delay and higher throughput. A Lagrangian Relaxation (LR)-based routing metric that considers the “system perspective” and “user perspective” is proposed to determine the near-optimal routing paths that satisfy end-to-end delay constraints with high system throughput. The empirical results show that the NODQC routing algorithm outperforms others in terms of higher system throughput with lower average end-to-end delay and delay jitter. In this paper, for the first time, the algorithm shows how to meet the delay QoS and at the same time how to achieve higher system throughput in stringently resource constrained WVSNs.

High resolution quantitative phase imaging of live cells with constrained optimization approach

NASA Astrophysics Data System (ADS)

Pandiyan, Vimal Prabhu; Khare, Kedar; John, Renu

2016-03-01

Quantitative phase imaging (QPI) aims at studying weakly scattering and absorbing biological specimens with subwavelength accuracy without any external staining mechanisms. Use of a reference beam at an angle is one of the necessary criteria for recording of high resolution holograms in most of the interferometric methods used for quantitative phase imaging. The spatial separation of the dc and twin images is decided by the reference beam angle and Fourier-filtered reconstructed image will have a very poor resolution if hologram is recorded below a minimum reference angle condition. However, it is always inconvenient to have a large reference beam angle while performing high resolution microscopy of live cells and biological specimens with nanometric features. In this paper, we treat reconstruction of digital holographic microscopy images as a constrained optimization problem with smoothness constraint in order to recover only complex object field in hologram plane even with overlapping dc and twin image terms. We solve this optimization problem by gradient descent approach iteratively and the smoothness constraint is implemented by spatial averaging with appropriate size. This approach will give excellent high resolution image recovery compared to Fourier filtering while keeping a very small reference angle. We demonstrate this approach on digital holographic microscopy of live cells by recovering the quantitative phase of live cells from a hologram recorded with nearly zero reference angle.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Spectral method for a kinetic swarming model

DOE PAGES

Gamba, Irene M.; Haack, Jeffrey R.; Motsch, Sebastien

2015-04-28

Here we present the first numerical method for a kinetic description of the Vicsek swarming model. The kinetic model poses a unique challenge, as there is a distribution dependent collision invariant to satisfy when computing the interaction term. We use a spectral representation linked with a discrete constrained optimization to compute these interactions. To test the numerical scheme we investigate the kinetic model at different scales and compare the solution with the microscopic and macroscopic descriptions of the Vicsek model. Lastly, we observe that the kinetic model captures key features such as vortex formation and traveling waves.

Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

Treesearch

Bistra Dilkina; Rachel Houtman; Carla P. Gomes; Claire A. Montgomery; Kevin S. McKelvey; Katherine Kendall; Tabitha A. Graves; Richard Bernstein; Michael K. Schwartz

2016-01-01

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a...

Style consistent classification of isogenous patterns.

PubMed

Sarkar, Prateek; Nagy, George

2005-01-01

In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.

JWST Wavefront Control Toolbox

NASA Technical Reports Server (NTRS)

Shin, Shahram Ron; Aronstein, David L.

2011-01-01

A Matlab-based toolbox has been developed for the wavefront control and optimization of segmented optical surfaces to correct for possible misalignments of James Webb Space Telescope (JWST) using influence functions. The toolbox employs both iterative and non-iterative methods to converge to an optimal solution by minimizing the cost function. The toolbox could be used in either of constrained and unconstrained optimizations. The control process involves 1 to 7 degrees-of-freedom perturbations per segment of primary mirror in addition to the 5 degrees of freedom of secondary mirror. The toolbox consists of a series of Matlab/Simulink functions and modules, developed based on a "wrapper" approach, that handles the interface and data flow between existing commercial optical modeling software packages such as Zemax and Code V. The limitations of the algorithm are dictated by the constraints of the moving parts in the mirrors.

Optical Correlation of Images With Signal-Dependent Noise Using Constrained-Modulation Filter Devices

NASA Technical Reports Server (NTRS)

Downie, John D.

1995-01-01

Images with signal-dependent noise present challenges beyond those of images with additive white or colored signal-independent noise in terms of designing the optimal 4-f correlation filter that maximizes correlation-peak signal-to-noise ratio, or combinations of correlation-peak metrics. Determining the proper design becomes more difficult when the filter is to be implemented on a constrained-modulation spatial light modulator device. The design issues involved for updatable optical filters for images with signal-dependent film-grain noise and speckle noise are examined. It is shown that although design of the optimal linear filter in the Fourier domain is impossible for images with signal-dependent noise, proper nonlinear preprocessing of the images allows the application of previously developed design rules for optimal filters to be implemented on constrained-modulation devices. Thus the nonlinear preprocessing becomes necessary for correlation in optical systems with current spatial light modulator technology. These results are illustrated with computer simulations of images with signal-dependent noise correlated with binary-phase-only filters and ternary-phase-amplitude filters.

Statistical model based iterative reconstruction in clinical CT systems. Part III. Task-based kV/mAs optimization for radiation dose reduction

PubMed Central

Li, Ke; Gomez-Cardona, Daniel; Hsieh, Jiang; Lubner, Meghan G.; Pickhardt, Perry J.; Chen, Guang-Hong

2015-01-01

Purpose: For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. Methods: The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d′(kV,mAs) is not lower than the prescribed value of d℞′ for a given imaging task. Since it is difficult to analytically model the dependence of d′ on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d′(kV,mAs) at a variety of kV–mAs combinations, after which the overlay of the dose contours and d′ contours is used to graphically determine the optimal kV–mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, d′ for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50–700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d℞′=16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. Results: For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d′ map and the iso-CNR map were the same for FBP; thus, the use of d′- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d′ and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA = 350, dose = 37.5 mGy; for MBIR, kV = 120, mA = 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. Conclusions: A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance. PMID:26328971

QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION

PubMed Central

Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy

2016-01-01

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method—named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)—for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results. PMID:26778864

Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Fang, Jing; Yuan, Jianping

2018-03-01

The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

An efficient and practical approach to obtain a better optimum solution for structural optimization

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu; Huang, Jyun-Hao

2013-08-01

For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.

Cost-optimized methods extending the solution space of lightweight spaceborne monolithic ZERODUR® mirrors to larger sizes

NASA Astrophysics Data System (ADS)

Leys, Antoine; Hull, Tony; Westerhoff, Thomas

2015-09-01

We address the problem that larger spaceborne mirrors require greater sectional thickness to achieve a sufficient first eigen frequency that is resilient to launch loads, and to be stable during optical telescope assembly integration and test, this added thickness results in unacceptable added mass if we simply scale up solutions for smaller mirrors. Special features, like cathedral ribs, arch, chamfers, and back-side following the contour of the mirror face have been considered for these studies. For computational efficiency, we have conducted detailed analysis on various configurations of a 800 mm hexagonal segment and of a 1.2-m mirror, in a manner that they can be constrained by manufacturing parameters as would be a 4-m mirror. Furthermore each model considered also has been constrained by cost-effective machining practice as defined in the SCHOTT Mainz factory. Analysis on variants of this 1.2-m mirror has shown a favorable configuration. We have then scaled this optimal configuration to 4-m aperture. We discuss resulting parameters of costoptimized 4-m mirrors. We also discuss the advantages and disadvantages this analysis reveals of going to cathedral rib architecture on 1-m class mirror substrates.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

PubMed Central

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-01-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements. PMID:27112127

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate.

PubMed

Pradines, Joël R; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-26

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

NASA Astrophysics Data System (ADS)

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratiomore » (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dufour, F., E-mail: dufour@math.u-bordeaux1.fr; Prieto-Rumeau, T., E-mail: tprieto@ccia.uned.es

We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state and action spaces, compact action sets, and lower semi-continuous cost functions. We introduce a set of hypotheses related to a positive weight function which allow us to consider cost functions that might not be bounded below by a constant, and which imply the solvability of the linear programming formulation of the constrained MDP. In particular, we establish the existence of a constrained optimal stationary policy. Our results are illustrated with an application to a fishery management problem.

Optimal lifting ascent trajectories for the space shuttle

NASA Technical Reports Server (NTRS)

Rau, T. R.; Elliott, J. R.

1972-01-01

The performance gains which are possible through the use of optimal trajectories for a particular space shuttle configuration are discussed. The spacecraft configurations and aerodynamic characteristics are described. Shuttle mission payload capability is examined with respect to the optimal orbit inclination for unconstrained, constrained, and nonlifting conditions. The effects of velocity loss and heating rate on the optimal ascent trajectory are investigated.

Detecting intention to execute the next movement while performing current movement from EEG using global optimal constrained ICA.

PubMed

Eilbeigi, Elnaz; Setarehdan, Seyed Kamaledin

2018-05-26

Brain-computer interfaces (BCIs) are a promising tool in neurorehabilitation. The intention to perform a motor action can be detected from brain signals and used to control robotic devices. Most previous studies have focused on the starting of movements from a resting state, while in daily life activities, motions occur continuously and the neural activities correlated to the evolving movements are yet to be investigated. First we investigate the existence of neural correlates of intention to replace an object on the table during a holding phase. Next, we present a new method to extract the movement-related cortical potentials (MRCP) from a single-trial EEG. A novel method called Global optimal constrained ICA (GocICA) is proposed to overcome the limitations of cICA which is implemented based on Particle Swarm Optimization (PSO) and Charged System Search (CSS) techniques. GocICA is then utilized for decoding the intention to grasp and lift and intention to replace movements where the results were compared. It was found that GocICA significantly improves the intention detection performance. Best results in offline detection were obtained with CSS-cICA for both kinds of intentions. Furthermore, pseudo-online decoding showed that GocICA was able to predict both intentions before the onset of related movements with the highest probability. Decoding of the next movement intention during current movement is possible, which can be used to create more natural neuroprostheses. The results demonstrate that GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of ERPs such as P300. Copyright © 2018 Elsevier Ltd. All rights reserved.

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

PubMed Central

Zhang, Zheng; Han, Xiao; Pearson, Erik; Pelizzari, Charles; Sidky, Emil Y; Pan, Xiaochuan

2017-01-01

Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp–Davis–Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration. PMID:27046218

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

Application of level set method to optimal vibration control of plate structures

NASA Astrophysics Data System (ADS)

Ansari, M.; Khajepour, A.; Esmailzadeh, E.

2013-02-01

Vibration control plays a crucial role in many structures, especially in the lightweight ones. One of the most commonly practiced method to suppress the undesirable vibration of structures is to attach patches of the constrained layer damping (CLD) onto the surface of the structure. In order to consider the weight efficiency of a structure, the best shapes and locations of the CLD patches should be determined to achieve the optimum vibration suppression with minimum usage of the CLD patches. This paper proposes a novel topology optimization technique that can determine the best shape and location of the applied CLD patches, simultaneously. Passive vibration control is formulated in the context of the level set method, which is a numerical technique to track shapes and locations concurrently. The optimal damping set could be found in a structure, in its fundamental vibration mode, such that the maximum modal loss factor of the system is achieved. Two different plate structures will be considered and the damping patches will be optimally located on them. At the same time, the best shapes of the damping patches will be determined too. In one example, the numerical results will be compared with those obtained from the experimental tests to validate the accuracy of the proposed method. This comparison reveals the effectiveness of the level set approach in finding the optimum shape and location of the CLD patches.

Program Aids Analysis And Optimization Of Design

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1994-01-01

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Optimal vibration control of a rotating plate with self-sensing active constrained layer damping

NASA Astrophysics Data System (ADS)

Xie, Zhengchao; Wong, Pak Kin; Lo, Kin Heng

2012-04-01

This paper proposes a finite element model for optimally controlled constrained layer damped (CLD) rotating plate with self-sensing technique and frequency-dependent material property in both the time and frequency domain. Constrained layer damping with viscoelastic material can effectively reduce the vibration in rotating structures. However, most existing research models use complex modulus approach to model viscoelastic material, and an additional iterative approach which is only available in frequency domain has to be used to include the material's frequency dependency. It is meaningful to model the viscoelastic damping layer in rotating part by using the anelastic displacement fields (ADF) in order to include the frequency dependency in both the time and frequency domain. Also, unlike previous ones, this finite element model treats all three layers as having the both shear and extension strains, so all types of damping are taken into account. Thus, in this work, a single layer finite element is adopted to model a three-layer active constrained layer damped rotating plate in which the constraining layer is made of piezoelectric material to work as both the self-sensing sensor and actuator under an linear quadratic regulation (LQR) controller. After being compared with verified data, this newly proposed finite element model is validated and could be used for future research.

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Model-Constrained Optimization Methods for Reduction of Parameterized Large-Scale Systems

DTIC Science & Technology

2007-05-01

advices on my newborn baby and for delicious Vietnamese food . Thanks to Chu Thim Ba Van for their hospitality that makes me feel at home whenever I...looking forward to enjoying the delicious food which she has cooked for me with all her love. More importantly, I would like to thank my wife for...working due to the intensive stress in finishing the thesis. It is amazing how a little three-month-old son can help his Dad release stress . Whenever I

Advances in Highly Constrained Multi-Phase Trajectory Generation using the General Pseudospectral Optimization Software (GPOPS)

DTIC Science & Technology

2013-08-01

release; distribution unlimited. PA Number 412-TW-PA-13395 f generic function g acceleration due to gravity h altitude L aerodynamic lift force L Lagrange...cost m vehicle mass M Mach number n number of coefficients in polynomial regression p highest order of polynomial regression Q dynamic pressure R...Method (RPM); the collocation points are defined by the roots of Legendre -Gauss- Radau (LGR) functions.9 GPOPS also automatically refines the “mesh” by

Dipole and quadrupole synthesis of electric potential fields. M.S. Thesis

NASA Technical Reports Server (NTRS)

Tilley, D. G.

1979-01-01

A general technique for expanding an unknown potential field in terms of a linear summation of weighted dipole or quadrupole fields is described. Computational methods were developed for the iterative addition of dipole fields. Various solution potentials were compared inside the boundary with a more precise calculation of the potential to derive optimal schemes for locating the singularities of the dipole fields. Then, the problem of determining solutions to Laplace's equation on an unbounded domain as constrained by pertinent electron trajectory data was considered.

Analytical approximation and numerical simulations for periodic travelling water waves

NASA Astrophysics Data System (ADS)

Kalimeris, Konstantinos

2017-12-01

We present recent analytical and numerical results for two-dimensional periodic travelling water waves with constant vorticity. The analytical approach is based on novel asymptotic expansions. We obtain numerical results in two different ways: the first is based on the solution of a constrained optimization problem, and the second is realized as a numerical continuation algorithm. Both methods are applied on some examples of non-constant vorticity. This article is part of the theme issue 'Nonlinear water waves'.

Mixed Integer PDE Constrained Optimization for the Control of a Wildfire Hazard

DTIC Science & Technology

2017-01-01

are nodes suitable for extinguishing the fire. We introduce a discretization of the time horizon [0, T] by the set of time T := {0, At,..., ntZ\\t = T...of the constraints and objective with a discrete counterpart. The PDE is replaced by a linear system obtained from a convergent finite difference...method [5] and the integral is replaced by a quadrature formula. The domain is discretized by replacing 17 with an equidistant grid of length Ax

Multidisciplinary Optimization Approach for Design and Operation of Constrained and Complex-shaped Space Systems

NASA Astrophysics Data System (ADS)

Lee, Dae Young

The design of a small satellite is challenging since they are constrained by mass, volume, and power. To mitigate these constraint effects, designers adopt deployable configurations on the spacecraft that result in an interesting and difficult optimization problem. The resulting optimization problem is challenging due to the computational complexity caused by the large number of design variables and the model complexity created by the deployables. Adding to these complexities, there is a lack of integration of the design optimization systems into operational optimization, and the utility maximization of spacecraft in orbit. The developed methodology enables satellite Multidisciplinary Design Optimization (MDO) that is extendable to on-orbit operation. Optimization of on-orbit operations is possible with MDO since the model predictive controller developed in this dissertation guarantees the achievement of the on-ground design behavior in orbit. To enable the design optimization of highly constrained and complex-shaped space systems, the spherical coordinate analysis technique, called the "Attitude Sphere", is extended and merged with an additional engineering tools like OpenGL. OpenGL's graphic acceleration facilitates the accurate estimation of the shadow-degraded photovoltaic cell area. This technique is applied to the design optimization of the satellite Electric Power System (EPS) and the design result shows that the amount of photovoltaic power generation can be increased more than 9%. Based on this initial methodology, the goal of this effort is extended from Single Discipline Optimization to Multidisciplinary Optimization, which includes the design and also operation of the EPS, Attitude Determination and Control System (ADCS), and communication system. The geometry optimization satisfies the conditions of the ground development phase; however, the operation optimization may not be as successful as expected in orbit due to disturbances. To address this issue, for the ADCS operations, controllers based on Model Predictive Control that are effective for constraint handling were developed and implemented. All the suggested design and operation methodologies are applied to a mission "CADRE", which is space weather mission scheduled for operation in 2016. This application demonstrates the usefulness and capability of the methodology to enhance CADRE's capabilities, and its ability to be applied to a variety of missions.

Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?

PubMed Central

Newberry, Mitchell G.; Savage, Van M.

2016-01-01

Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the human head and torso and of a mouse lung based on three-dimensional images processed via our software Angicart. In contrast to modern allometric theories, we find systematic patterns of asymmetry in vascular branching, potentially explaining previously documented mismatches between predictions (power-law or concave curvature) and observed empirical data (convex curvature) for the allometric scaling of metabolic rate. To examine why these systematic asymmetries in vascular branching might arise, we construct a mathematical framework to derive predictions based on local, junction-level optimality principles that have been proposed to be favored in the course of natural selection and development. The two most commonly used principles are material-cost optimizations (construction materials or blood volume) and optimization of efficient flow via minimization of power loss. We show that material-cost optimization solutions match with distributions for asymmetric branching across the whole network but do not match well for individual junctions. Consequently, we also explore random branching that is constrained at scales that range from local (junction-level) to global (whole network). We find that material-cost optimizations are the strongest predictor of vascular branching in the human head and torso, whereas locally or intermediately constrained random branching is comparable to material-cost optimizations for the mouse lung. These differences could be attributable to developmentally-programmed local branching for larger vessels and constrained random branching for smaller vessels. PMID:27902691

Design of a composite filter realizable on practical spatial light modulators

NASA Technical Reports Server (NTRS)

Rajan, P. K.; Ramakrishnan, Ramachandran

1994-01-01

Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.

Adjoint assimilation of altimetric, surface drifter, and hydrographic data in a quasi-geostrophic model of the Azores Current

NASA Astrophysics Data System (ADS)

Morrow, Rosemary; de Mey, Pierre

1995-12-01

The flow characteristics in the region of the Azores Current are investigated by assimilating TOPEX/POSEIDON and ERS 1 altimeter data into the multilevel Harvard quasigeostrophic (QG) model with open boundaries (Miller et al., 1983) using an adjoint variational scheme (Moore, 1991). The study site lies in the path of the Azores Current, where a branch retroflects to the south in the vicinity of the Madeira Rise. The region was the site of an intensive field program in 1993, SEMAPHORE. We had two main aims in this adjoint assimilation project. The first was to see whether the adjoint method could be applied locally to optimize an initial guess field, derived from the continous assimilation of altimetry data using optimal interpolation (OI). The second aim was to assimilate a variety of different data sets and evaluate their importance in constraining our QG model. The adjoint assimilation of surface data was effective in optimizing the initial conditions from OI. After 20 iterations the cost function was generally reduced by 50-80%, depending on the chosen data constraints. The primary adjustment process was via the barotropic mode. Altimetry proved to be a good constraint on the variable flow field, in particular, for constraining the barotropic field. The excellent data quality of the TOPEX/POSEIDON (T/P) altimeter data provided smooth and reliable forcing; but for our mesoscale study in a region of long decorrelation times O(30 days), the spatial coverage from the combined T/P and ERS 1 data sets was more important for constraining the solution and providing stable flow at all levels. Surface drifters provided an excellent constraint on both the barotropic and baroclinic model fields. More importantly, the drifters provided a reliable measure of the mean field. Hydrographic data were also applied as a constraint; in general, hydrography provided a weak but effective constraint on the vertical Rossby modes in the model. Finally, forecasts run over a 2-month period indicate that the initial conditions optimized by the 20-day adjoint assimilation provide more stable, longer-term forecasts.

Manifold Learning by Preserving Distance Orders.

PubMed

Ataer-Cansizoglu, Esra; Akcakaya, Murat; Orhan, Umut; Erdogmus, Deniz

2014-03-01

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

A zonal computational procedure adapted to the optimization of two-dimensional thrust augmentor inlets

NASA Technical Reports Server (NTRS)

Lund, T. S.; Tavella, D. A.; Roberts, L.

1985-01-01

A viscous-inviscid interaction methodology based on a zonal description of the flowfield is developed as a mean of predicting the performance of two-dimensional thrust augmenting ejectors. An inviscid zone comprising the irrotational flow about the device is patched together with a viscous zone containing the turbulent mixing flow. The inviscid region is computed by a higher order panel method, while an integral method is used for the description of the viscous part. A non-linear, constrained optimization study is undertaken for the design of the inlet region. In this study, the viscous-inviscid analysis is complemented with a boundary layer calculation to account for flow separation from the walls of the inlet region. The thrust-based Reynolds number as well as the free stream velocity are shown to be important parameters in the design of a thrust augmentor inlet.

SLFP: a stochastic linear fractional programming approach for sustainable waste management.

PubMed

Zhu, H; Huang, G H

2011-12-01

A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk. Copyright © 2011 Elsevier Ltd. All rights reserved.

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2016-08-31

In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

LDRD Report: Topological Design Optimization of Convolutes in Next Generation Pulsed Power Devices.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cyr, Eric C.; von Winckel, Gregory John; Kouri, Drew Philip

This LDRD project was developed around the ambitious goal of applying PDE-constrained opti- mization approaches to design Z-machine components whose performance is governed by elec- tromagnetic and plasma models. This report documents the results of this LDRD project. Our differentiating approach was to use topology optimization methods developed for structural design and extend them for application to electromagnetic systems pertinent to the Z-machine. To achieve this objective a suite of optimization algorithms were implemented in the ROL library part of the Trilinos framework. These methods were applied to standalone demonstration problems and the Drekar multi-physics research application. Out of thismore » exploration a new augmented Lagrangian approach to structural design problems was developed. We demonstrate that this approach has favorable mesh-independent performance. Both the final design and the algorithmic performance were independent of the size of the mesh. In addition, topology optimization formulations for the design of conducting networks were developed and demonstrated. Of note, this formulation was used to develop a design for the inner magnetically insulated transmission line on the Z-machine. The resulting electromagnetic device is compared with theoretically postulated designs.« less

Comparison of optimization algorithms in intensity-modulated radiation therapy planning

NASA Astrophysics Data System (ADS)

Kendrick, Rachel

Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.

Point-based warping with optimized weighting factors of displacement vectors

NASA Astrophysics Data System (ADS)

Pielot, Ranier; Scholz, Michael; Obermayer, Klaus; Gundelfinger, Eckart D.; Hess, Andreas

2000-06-01

The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.

A Path Algorithm for Constrained Estimation

PubMed Central

Zhou, Hua; Lange, Kenneth

2013-01-01

Many least-square problems involve affine equality and inequality constraints. Although there are a variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current article proposes a new path-following algorithm for quadratic programming that replaces hard constraints by what are called exact penalties. Similar penalties arise in l1 regularization in model selection. In the regularization setting, penalties encapsulate prior knowledge, and penalized parameter estimates represent a trade-off between the observed data and the prior knowledge. Classical penalty methods of optimization, such as the quadratic penalty method, solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties!are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path-following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in Lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the Lasso and generalized Lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well-chosen examples illustrate the mechanics and potential of path following. This article has supplementary materials available online. PMID:24039382

Methods of sequential estimation for determining initial data in numerical weather prediction. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cohn, S. E.

1982-01-01

Numerical weather prediction (NWP) is an initial-value problem for a system of nonlinear differential equations, in which initial values are known incompletely and inaccurately. Observational data available at the initial time must therefore be supplemented by data available prior to the initial time, a problem known as meteorological data assimilation. A further complication in NWP is that solutions of the governing equations evolve on two different time scales, a fast one and a slow one, whereas fast scale motions in the atmosphere are not reliably observed. This leads to the so called initialization problem: initial values must be constrained to result in a slowly evolving forecast. The theory of estimation of stochastic dynamic systems provides a natural approach to such problems. For linear stochastic dynamic models, the Kalman-Bucy (KB) sequential filter is the optimal data assimilation method, for linear models, the optimal combined data assimilation-initialization method is a modified version of the KB filter.

Temporally diffeomorphic cardiac motion estimation from three-dimensional echocardiography by minimization of intensity consistency error.

PubMed

Zhang, Zhijun; Ashraf, Muhammad; Sahn, David J; Song, Xubo

2014-05-01

Quantitative analysis of cardiac motion is important for evaluation of heart function. Three dimensional (3D) echocardiography is among the most frequently used imaging modalities for motion estimation because it is convenient, real-time, low-cost, and nonionizing. However, motion estimation from 3D echocardiographic sequences is still a challenging problem due to low image quality and image corruption by noise and artifacts. The authors have developed a temporally diffeomorphic motion estimation approach in which the velocity field instead of the displacement field was optimized. The optimal velocity field optimizes a novel similarity function, which we call the intensity consistency error, defined as multiple consecutive frames evolving to each time point. The optimization problem is solved by using the steepest descent method. Experiments with simulated datasets, images of anex vivo rabbit phantom, images of in vivo open-chest pig hearts, and healthy human images were used to validate the authors' method. Simulated and real cardiac sequences tests showed that results in the authors' method are more accurate than other competing temporal diffeomorphic methods. Tests with sonomicrometry showed that the tracked crystal positions have good agreement with ground truth and the authors' method has higher accuracy than the temporal diffeomorphic free-form deformation (TDFFD) method. Validation with an open-access human cardiac dataset showed that the authors' method has smaller feature tracking errors than both TDFFD and frame-to-frame methods. The authors proposed a diffeomorphic motion estimation method with temporal smoothness by constraining the velocity field to have maximum local intensity consistency within multiple consecutive frames. The estimated motion using the authors' method has good temporal consistency and is more accurate than other temporally diffeomorphic motion estimation methods.

N-representability-driven reconstruction of the two-electron reduced-density matrix for a real-time time-dependent electronic structure method

NASA Astrophysics Data System (ADS)

Jeffcoat, David B.; DePrince, A. Eugene

2014-12-01

Propagating the equations of motion (EOM) for the one-electron reduced-density matrix (1-RDM) requires knowledge of the corresponding two-electron RDM (2-RDM). We show that the indeterminacy of this expression can be removed through a constrained optimization that resembles the variational optimization of the ground-state 2-RDM subject to a set of known N-representability conditions. Electronic excitation energies can then be obtained by propagating the EOM for the 1-RDM and following the dipole moment after the system interacts with an oscillating external electric field. For simple systems with well-separated excited states whose symmetry differs from that of the ground state, excitation energies obtained from this method are comparable to those obtained from full configuration interaction computations. Although the optimized 2-RDM satisfies necessary N-representability conditions, the procedure cannot guarantee a unique mapping from the 1-RDM to the 2-RDM. This deficiency is evident in the mean-field-quality description of transitions to states of the same symmetry as the ground state, as well as in the inability of the method to describe Rabi oscillations.

Structural synthesis: Precursor and catalyst

NASA Technical Reports Server (NTRS)

Schmit, L. A.

1984-01-01

More than twenty five years have elapsed since it was recognized that a rather general class of structural design optimization tasks could be properly posed as an inequality constrained minimization problem. It is suggested that, independent of primary discipline area, it will be useful to think about: (1) posing design problems in terms of an objective function and inequality constraints; (2) generating design oriented approximate analysis methods (giving special attention to behavior sensitivity analysis); (3) distinguishing between decisions that lead to an analysis model and those that lead to a design model; (4) finding ways to generate a sequence of approximate design optimization problems that capture the essential characteristics of the primary problem, while still having an explicit algebraic form that is matched to one or more of the established optimization algorithms; (5) examining the potential of optimum design sensitivity analysis to facilitate quantitative trade-off studies as well as participation in multilevel design activities. It should be kept in mind that multilevel methods are inherently well suited to a parallel mode of operation in computer terms or to a division of labor between task groups in organizational terms. Based on structural experience with multilevel methods general guidelines are suggested.

Application of Sequential Quadratic Programming to Minimize Smart Active Flap Rotor Hub Loads

NASA Technical Reports Server (NTRS)

Kottapalli, Sesi; Leyland, Jane

2014-01-01

In an analytical study, SMART active flap rotor hub loads have been minimized using nonlinear programming constrained optimization methodology. The recently developed NLPQLP system (Schittkowski, 2010) that employs Sequential Quadratic Programming (SQP) as its core algorithm was embedded into a driver code (NLP10x10) specifically designed to minimize active flap rotor hub loads (Leyland, 2014). Three types of practical constraints on the flap deflections have been considered. To validate the current application, two other optimization methods have been used: i) the standard, linear unconstrained method, and ii) the nonlinear Generalized Reduced Gradient (GRG) method with constraints. The new software code NLP10x10 has been systematically checked out. It has been verified that NLP10x10 is functioning as desired. The following are briefly covered in this paper: relevant optimization theory; implementation of the capability of minimizing a metric of all, or a subset, of the hub loads as well as the capability of using all, or a subset, of the flap harmonics; and finally, solutions for the SMART rotor. The eventual goal is to implement NLP10x10 in a real-time wind tunnel environment.

Sound reproduction in personal audio systems using the least-squares approach with acoustic contrast control constraint.

PubMed

Cai, Yefeng; Wu, Ming; Yang, Jun

2014-02-01

This paper describes a method for focusing the reproduced sound in the bright zone without disturbing other people in the dark zone in personal audio systems. The proposed method combines the least-squares and acoustic contrast criteria. A constrained parameter is introduced to tune the balance between two performance indices, namely, the acoustic contrast and the spatial average error. An efficient implementation of this method using convex optimization is presented. Offline simulations and real-time experiments using a linear loudspeaker array are conducted to evaluate the performance of the presented method. Results show that compared with the traditional acoustic contrast control method, the proposed method can improve the flatness of response in the bright zone by sacrificing the level of acoustic contrast.

Conformationally constrained farnesoid X receptor (FXR) agonists: alternative replacements of the stilbene.

PubMed

Akwabi-Ameyaw, Adwoa; Caravella, Justin A; Chen, Lihong; Creech, Katrina L; Deaton, David N; Madauss, Kevin P; Marr, Harry B; Miller, Aaron B; Navas, Frank; Parks, Derek J; Spearing, Paul K; Todd, Dan; Williams, Shawn P; Wisely, G Bruce

2011-10-15

To further explore the optimum placement of the acid moiety in conformationally constrained analogs of GW 4064 1a, a series of stilbene replacements were prepared. The benzothiophene 1f and the indole 1g display the optimal orientation of the carboxylate for enhanced FXR agonist potency. Copyright © 2011 Elsevier Ltd. All rights reserved.

Joint terminals and relay optimization for two-way power line information exchange systems with QoS constraints

NASA Astrophysics Data System (ADS)

Wu, Xiaolin; Rong, Yue

2015-12-01

The quality-of-service (QoS) criteria (measured in terms of the minimum capacity requirement in this paper) are very important to practical indoor power line communication (PLC) applications as they greatly affect the user experience. With a two-way multicarrier relay configuration, in this paper we investigate the joint terminals and relay power optimization for the indoor broadband PLC environment, where the relay node works in the amplify-and-forward (AF) mode. As the QoS-constrained power allocation problem is highly non-convex, the globally optimal solution is computationally intractable to obtain. To overcome this challenge, we propose an alternating optimization (AO) method to decompose this problem into three convex/quasi-convex sub-problems. Simulation results demonstrate the fast convergence of the proposed algorithm under practical PLC channel conditions. Compared with the conventional bidirectional direct transmission (BDT) system, the relay-assisted two-way information exchange (R2WX) scheme can meet the same QoS requirement with less total power consumption.

Optimization for Service Routes of Pallet Service Center Based on the Pallet Pool Mode

PubMed Central

He, Shiwei; Song, Rui

2016-01-01

Service routes optimization (SRO) of pallet service center should meet customers' demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case. PMID:27528865

Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fredriksson, Albin, E-mail: albin.fredriksson@raysearchlabs.com; Hårdemark, Björn; Forsgren, Anders

2015-07-15

Purpose: This paper introduces a method that maximizes the probability of satisfying the clinical goals in intensity-modulated radiation therapy treatments subject to setup uncertainty. Methods: The authors perform robust optimization in which the clinical goals are constrained to be satisfied whenever the setup error falls within an uncertainty set. The shape of the uncertainty set is included as a variable in the optimization. The goal of the optimization is to modify the shape of the uncertainty set in order to maximize the probability that the setup error will fall within the modified set. Because the constraints enforce the clinical goalsmore » to be satisfied under all setup errors within the uncertainty set, this is equivalent to maximizing the probability of satisfying the clinical goals. This type of robust optimization is studied with respect to photon and proton therapy applied to a prostate case and compared to robust optimization using an a priori defined uncertainty set. Results: Slight reductions of the uncertainty sets resulted in plans that satisfied a larger number of clinical goals than optimization with respect to a priori defined uncertainty sets, both within the reduced uncertainty sets and within the a priori, nonreduced, uncertainty sets. For the prostate case, the plans taking reduced uncertainty sets into account satisfied 1.4 (photons) and 1.5 (protons) times as many clinical goals over the scenarios as the method taking a priori uncertainty sets into account. Conclusions: Reducing the uncertainty sets enabled the optimization to find better solutions with respect to the errors within the reduced as well as the nonreduced uncertainty sets and thereby achieve higher probability of satisfying the clinical goals. This shows that asking for a little less in the optimization sometimes leads to better overall plan quality.« less

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

PubMed

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Optimization Control of the Color-Coating Production Process for Model Uncertainty

PubMed Central

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. PMID:27247563

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

PubMed

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.

Constraining gross primary production and ecosystem respiration estimates for North America using atmospheric observations of carbonyl sulfide (OCS) and CO2

NASA Astrophysics Data System (ADS)

He, W.; Ju, W.; Chen, H.; Peters, W.; van der Velde, I.; Baker, I. T.; Andrews, A. E.; Zhang, Y.; Launois, T.; Campbell, J. E.; Suntharalingam, P.; Montzka, S. A.

2016-12-01

Carbonyl sulfide (OCS) is a promising novel atmospheric tracer for studying carbon cycle processes. OCS shares a similar pathway as CO2 during photosynthesis but not released through a respiration-like process, thus could be used to partition Gross Primary Production (GPP) from Net Ecosystem-atmosphere CO2 Exchange (NEE). This study uses joint atmospheric observations of OCS and CO2 to constrain GPP and ecosystem respiration (Re). Flask data from tower and aircraft sites over North America are collected. We employ our recently developed CarbonTracker (CT)-Lagrange carbon assimilation system, which is based on the CT framework and the Weather Research and Forecasting - Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model, and the Simple Biosphere model with simulated OCS (SiB3-OCS) that provides prior GPP, Re and plant uptake fluxes of OCS. Derived plant OCS fluxes from both process model and GPP-scaled model are tested in our inversion. To investigate the ability of OCS to constrain GPP and understand the uncertainty propagated from OCS modeling errors to constrained fluxes in a dual-tracer system including OCS and CO2, two inversion schemes are implemented and compared: (1) a two-step scheme, which firstly optimizes GPP using OCS observations, and then simultaneously optimizes GPP and Re using CO2 observations with OCS-constrained GPP in the first step as prior; (2) a joint scheme, which simultaneously optimizes GPP and Re using OCS and CO2 observations. We will evaluate the result using an estimated GPP from space-borne solar-induced fluorescence observations and a data-driven GPP upscaled from FLUXNET data with a statistical model (Jung et al., 2011). Preliminary result for the year 2010 shows the joint inversion makes simulated mole fractions more consistent with observations for both OCS and CO2. However, the uncertainty of OCS simulation is larger than that of CO2. The two-step and joint schemes perform similarly in improving the consistence with observations for OCS, implicating that OCS could provide independent constraint in joint inversion. Optimization makes less total GPP and Re but more NEE, when testing with prior CO2 fluxes from two biosphere models. This study gives an in-depth insight into the role of joint atmospheric OCS and CO2 observations in constraining CO2 fluxes.

Cascaded Optimization for a Persistent Data Ferrying Unmanned Aircraft

NASA Astrophysics Data System (ADS)

Carfang, Anthony

This dissertation develops and assesses a cascaded method for designing optimal periodic trajectories and link schedules for an unmanned aircraft to ferry data between stationary ground nodes. This results in a fast solution method without the need to artificially constrain system dynamics. Focusing on a fundamental ferrying problem that involves one source and one destination, but includes complex vehicle and Radio-Frequency (RF) dynamics, a cascaded structure to the system dynamics is uncovered. This structure is exploited by reformulating the nonlinear optimization problem into one that reduces the independent control to the vehicle's motion, while the link scheduling control is folded into the objective function and implemented as an optimal policy that depends on candidate motion control. This formulation is proven to maintain optimality while reducing computation time in comparison to traditional ferry optimization methods. The discrete link scheduling problem takes the form of a combinatorial optimization problem that is known to be NP-Hard. A derived necessary condition for optimality guides the development of several heuristic algorithms, specifically the Most-Data-First Algorithm and the Knapsack Adaptation. These heuristics are extended to larger ferrying scenarios, and assessed analytically and through Monte Carlo simulation, showing better throughput performance in the same order of magnitude of computation time in comparison to other common link scheduling policies. The cascaded optimization method is implemented with a novel embedded software system on a small, unmanned aircraft to validate the simulation results with field experiments. To address the sensitivity of results on trajectory tracking performance, a system that combines motion and link control with waypoint-based navigation is developed and assessed through field experiments. The data ferrying algorithms are further extended by incorporating a Gaussian process to opportunistically learn the RF environment. By continuously improving RF models, the cascaded planner can continually improve the ferrying system's overall performance.

Analysis of explicit model predictive control for path-following control

PubMed Central

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080

Analysis of explicit model predictive control for path-following control.

PubMed

Lee, Junho; Chang, Hyuk-Jun

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

A bat algorithm with mutation for UCAV path planning.

PubMed

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roald, Line; Misra, Sidhant; Krause, Thilo

Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty asmore » continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. Lastly, by applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.« less

Measuring the Redshift Dependence of The Cosmic Microwave Background Monopole Temperature With Planck Data

NASA Technical Reports Server (NTRS)

De Martino, I.; Atrio-Barandela, F.; Da Silva, A.; Ebling, H.; Kashlinsky, A.; Kocevski, D.; Martins, C. J. A. P.

2012-01-01

We study the capability of Planck data to constrain deviations of the cosmic microwave background (CMB) blackbody temperature from adiabatic evolution using the thermal Sunyaev-Zeldovich anisotropy induced by clusters of galaxies. We consider two types of data sets depending on how the cosmological signal is removed: using a CMB template or using the 217 GHz map. We apply two different statistical estimators, based on the ratio of temperature anisotropies at two different frequencies and on a fit to the spectral variation of the cluster signal with frequency. The ratio method is biased if CMB residuals with amplitude approximately 1 microK or larger are present in the data, while residuals are not so critical for the fit method. To test for systematics, we construct a template from clusters drawn from a hydro-simulation included in the pre-launch Planck Sky Model. We demonstrate that, using a proprietary catalog of X-ray-selected clusters with measured redshifts, electron densities, and X-ray temperatures, we can constrain deviations of adiabatic evolution, measured by the parameter a in the redshift scaling T (z) = T0(1 + z)(sup 1-alpha), with an accuracy of sigma(sub alpha) = 0.011 in the most optimal case and with sigma alpha = 0.018 for a less optimal case. These results represent a factor of 2-3 improvement over similar measurements carried out using quasar spectral lines and a factor 6-20 with respect to earlier results using smaller cluster samples.

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

DOE PAGES

Roald, Line; Misra, Sidhant; Krause, Thilo; ...

2016-08-25

Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty asmore » continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. Lastly, by applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.« less

An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis

NASA Astrophysics Data System (ADS)

Kang, Fei; Li, Junjie; Ma, Zhenyue

2013-02-01

Determination of the critical slip surface with the minimum factor of safety of a slope is a difficult constrained global optimization problem. In this article, an artificial bee colony algorithm with a multi-slice adjustment method is proposed for locating the critical slip surfaces of soil slopes, and the Spencer method is employed to calculate the factor of safety. Six benchmark examples are presented to illustrate the reliability and efficiency of the proposed technique, and it is also compared with some well-known or recent algorithms for the problem. The results show that the new algorithm is promising in terms of accuracy and efficiency.

An optimization-based approach for high-order accurate discretization of conservation laws with discontinuous solutions

NASA Astrophysics Data System (ADS)

Zahr, M. J.; Persson, P.-O.

2018-07-01

This work introduces a novel discontinuity-tracking framework for resolving discontinuous solutions of conservation laws with high-order numerical discretizations that support inter-element solution discontinuities, such as discontinuous Galerkin or finite volume methods. The proposed method aims to align inter-element boundaries with discontinuities in the solution by deforming the computational mesh. A discontinuity-aligned mesh ensures the discontinuity is represented through inter-element jumps while smooth basis functions interior to elements are only used to approximate smooth regions of the solution, thereby avoiding Gibbs' phenomena that create well-known stability issues. Therefore, very coarse high-order discretizations accurately resolve the piecewise smooth solution throughout the domain, provided the discontinuity is tracked. Central to the proposed discontinuity-tracking framework is a discrete PDE-constrained optimization formulation that simultaneously aligns the computational mesh with discontinuities in the solution and solves the discretized conservation law on this mesh. The optimization objective is taken as a combination of the deviation of the finite-dimensional solution from its element-wise average and a mesh distortion metric to simultaneously penalize Gibbs' phenomena and distorted meshes. It will be shown that our objective function satisfies two critical properties that are required for this discontinuity-tracking framework to be practical: (1) possesses a local minima at a discontinuity-aligned mesh and (2) decreases monotonically to this minimum in a neighborhood of radius approximately h / 2, whereas other popular discontinuity indicators fail to satisfy the latter. Another important contribution of this work is the observation that traditional reduced space PDE-constrained optimization solvers that repeatedly solve the conservation law at various mesh configurations are not viable in this context since severe overshoot and undershoot in the solution, i.e., Gibbs' phenomena, may make it impossible to solve the discrete conservation law on non-aligned meshes. Therefore, we advocate a gradient-based, full space solver where the mesh and conservation law solution converge to their optimal values simultaneously and therefore never require the solution of the discrete conservation law on a non-aligned mesh. The merit of the proposed method is demonstrated on a number of one- and two-dimensional model problems including the L2 projection of discontinuous functions, Burgers' equation with a discontinuous source term, transonic flow through a nozzle, and supersonic flow around a bluff body. We demonstrate optimal O (h p + 1) convergence rates in the L1 norm for up to polynomial order p = 6 and show that accurate solutions can be obtained on extremely coarse meshes.

Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.

PubMed

Cui, Chen; Wu, Xiaodong; Newell, John D; Jacob, Mathews

2015-03-01

This article focuses on developing a novel noniterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map-induced frequency shift between adjacent voxels are constrained to be in a finite range. The discretization of the above problem yields a graph optimization scheme, where each node of the graph is only connected with few other nodes. Thanks to the low graph connectivity, the problem is solved efficiently using a noniterative graph cut algorithm. The global minimum of the constrained optimization problem is guaranteed. The performance of the algorithm is compared with that of state-of-the-art schemes. Quantitative comparisons are also made against reference data. The proposed algorithm is observed to yield more robust fat water estimates with fewer fat water swaps and better quantitative results than other state-of-the-art algorithms in a range of challenging applications. The proposed algorithm is capable of considerably reducing the swaps in challenging fat water decomposition problems. The experiments demonstrate the benefit of using explicit smoothness constraints in field map estimation and solving the problem using a globally convergent graph-cut optimization algorithm. © 2014 Wiley Periodicals, Inc.

Robust Constrained Blackbox Optimization with Surrogates

DTIC Science & Technology

2015-05-21

algorithms with OPAL . Mathematical Programming Computation, 6(3):233–254, 2014. 6. M.S. Ouali, H. Aoudjit, and C. Audet. Replacement scheduling of a fleet of...Orban. Optimization of Algorithms with OPAL . Mathematical Programming Computation, 6(3), 233-254, September 2014. DISTRIBUTION A: Distribution

A Probabilistic Approach to Fitting Period–luminosity Relations and Validating Gaia Parallaxes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sesar, Branimir; Fouesneau, Morgan; Bailer-Jones, Coryn A. L.

Pulsating stars, such as Cepheids, Miras, and RR Lyrae stars, are important distance indicators and calibrators of the “cosmic distance ladder,” and yet their period–luminosity–metallicity (PLZ) relations are still constrained using simple statistical methods that cannot take full advantage of available data. To enable optimal usage of data provided by the Gaia mission, we present a probabilistic approach that simultaneously constrains parameters of PLZ relations and uncertainties in Gaia parallax measurements. We demonstrate this approach by constraining PLZ relations of type ab RR Lyrae stars in near-infrared W 1 and W 2 bands, using Tycho- Gaia Astrometric Solution (TGAS) parallaxmore » measurements for a sample of ≈100 type ab RR Lyrae stars located within 2.5 kpc of the Sun. The fitted PLZ relations are consistent with previous studies, and in combination with other data, deliver distances precise to 6% (once various sources of uncertainty are taken into account). To a precision of 0.05 mas (1 σ ), we do not find a statistically significant offset in TGAS parallaxes for this sample of distant RR Lyrae stars (median parallax of 0.8 mas and distance of 1.4 kpc). With only minor modifications, our probabilistic approach can be used to constrain PLZ relations of other pulsating stars, and we intend to apply it to Cepheid and Mira stars in the near future.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Unkelbach, Jan, E-mail: junkelbach@mgh.harvard.edu; Botas, Pablo; Faculty of Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg

Purpose: We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution. Methods and Materials: To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dosemore » objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET. Results: The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.« less

Use of system identification techniques for improving airframe finite element models using test data

NASA Technical Reports Server (NTRS)

Hanagud, Sathya V.; Zhou, Weiyu; Craig, James I.; Weston, Neil J.

1991-01-01

A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

An augmented Lagrangian trust region method for inclusion boundary reconstruction using ultrasound/electrical dual-modality tomography

NASA Astrophysics Data System (ADS)

Liang, Guanghui; Ren, Shangjie; Dong, Feng

2018-07-01

The ultrasound/electrical dual-modality tomography utilizes the complementarity of ultrasound reflection tomography (URT) and electrical impedance tomography (EIT) to improve the speed and accuracy of image reconstruction. Due to its advantages of no-invasive, no-radiation and low-cost, ultrasound/electrical dual-modality tomography has attracted much attention in the field of dual-modality imaging and has many potential applications in industrial and biomedical imaging. However, the data fusion of URT and EIT is difficult due to their different theoretical foundations and measurement principles. The most commonly used data fusion strategy in ultrasound/electrical dual-modality tomography is incorporating the structured information extracted from the URT into the EIT image reconstruction process through a pixel-based constraint. Due to the inherent non-linearity and ill-posedness of EIT, the reconstructed images from the strategy suffer from the low resolution, especially at the boundary of the observed inclusions. To improve this condition, an augmented Lagrangian trust region method is proposed to directly reconstruct the shapes of the inclusions from the ultrasound/electrical dual-modality measurements. In the proposed method, the shape of the target inclusion is parameterized by a radial shape model whose coefficients are used as the shape parameters. Then, the dual-modality shape inversion problem is formulated by an energy minimization problem in which the energy function derived from EIT is constrained by an ultrasound measurements model through an equality constraint equation. Finally, the optimal shape parameters associated with the optimal inclusion shape guesses are determined by minimizing the constrained cost function using the augmented Lagrangian trust region method. To evaluate the proposed method, numerical tests are carried out. Compared with single modality EIT, the proposed dual-modality inclusion boundary reconstruction method has a higher accuracy and is more robust to the measurement noise.

Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task.

PubMed

Bonnet, Vincent; Azevedo-Coste, Christine; Robert, Thomas; Fraisse, Philippe; Venture, Gentiane

2017-07-01

This paper aims at developing and evaluating a new practical method for the real-time estimate of joint torques and external wrenches during multi-contact sit-to-stand (STS) task using kinematics data only. The proposed method allows also identifying subject specific body inertial segment parameters that are required to perform inverse dynamics. The identification phase is performed using simple and repeatable motions. Thanks to an accurately identified model the estimate of the total external wrench can be used as an input to solve an under-determined multi-contact problem. It is solved using a constrained quadratic optimization process minimizing a hybrid human-like energetic criterion. The weights of this hybrid cost function are adjusted and a sensitivity analysis is performed in order to reproduce robustly human external wrench distribution. The results showed that the proposed method could successfully estimate the external wrenches under buttocks, feet, and hands during STS tasks (RMS error lower than 20 N and 6 N.m). The simplicity and generalization abilities of the proposed method allow paving the way of future diagnosis solutions and rehabilitation applications, including in-home use.

Optimal groundwater security management policies by control of inexact health risks under dual uncertainty in slope factors.

PubMed

Lu, Hongwei; Li, Jing; Ren, Lixia; Chen, Yizhong

2018-05-01

Groundwater remediation is a complicated system with time-consuming and costly challenges, which should be carefully controlled by appropriate groundwater management. This study develops an integrated optimization method for groundwater remediation management regarding cost, contamination distribution and health risk under multiple uncertainties. The integration of health risk into groundwater remediation optimization management is capable of not only adequately considering the influence of health risk on optimal remediation strategies, but also simultaneously completing remediation optimization design and risk assessment. A fuzzy chance-constrained programming approach is presented to handle multiple uncertain properties in the process of health risk assessment. The capabilities and effectiveness of the developed method are illustrated through an application of a naphthalene contaminated case in Anhui, China. Results indicate that (a) the pump-and-treat remediation system leads to a low naphthalene contamination but high remediation cost for a short-time remediation, and natural attenuation significantly affects naphthalene removal from groundwater for a long-time remediation; (b) the weighting coefficients have significant influences on the remediation cost and the performances both for naphthalene concentrations and health risks; (c) an increased level of slope factor (sf) for naphthalene corresponds to more optimal strategies characterized by higher environmental benefits and lower economic sacrifice. The developed method could be simultaneously beneficial for public health and environmental protection. Decision makers could obtain the most appropriate remediation strategies according to their specific requirements with high flexibility of economic, environmental, and risk concerns. Copyright © 2018 Elsevier Ltd. All rights reserved.

Number-unconstrained quantum sensing

NASA Astrophysics Data System (ADS)

Mitchell, Morgan W.

2017-12-01

Quantum sensing is commonly described as a constrained optimization problem: maximize the information gained about an unknown quantity using a limited number of particles. Important sensors including gravitational wave interferometers and some atomic sensors do not appear to fit this description, because there is no external constraint on particle number. Here, we develop the theory of particle-number-unconstrained quantum sensing, and describe how optimal particle numbers emerge from the competition of particle-environment and particle-particle interactions. We apply the theory to optical probing of an atomic medium modeled as a resonant, saturable absorber, and observe the emergence of well-defined finite optima without external constraints. The results contradict some expectations from number-constrained quantum sensing and show that probing with squeezed beams can give a large sensitivity advantage over classical strategies when each is optimized for particle number.

Women in Transition: Experiences of Health and Health Care for Recently Incarcerated Women Living in Community Corrections Facilities.

PubMed

Colbert, Alison M; Goshin, Lorie S; Durand, Vanessa; Zoucha, Rick; Sekula, L Kathleen

2016-12-01

Health priorities of women after incarceration remain poorly understood, constraining development of interventions targeted at their health during that time. We explored the experience of health and health care after incarceration in a focused ethnography of 28 women who had been released from prison or jail within the past year and were living in community corrections facilities. The women's outlook on health was rooted in a newfound core optimism, but this was constrained by their pressing health-related issues; stress and uncertainty; and the pressures of the criminal justice system. These external forces threatened to cause collapse of women's core optimism. Findings support interventions that capitalize on women's optimism and address barriers specific to criminal justice contexts. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Maximum entropy production: Can it be used to constrain conceptual hydrological models?

Treesearch

M.C. Westhoff; E. Zehe

2013-01-01

In recent years, optimality principles have been proposed to constrain hydrological models. The principle of maximum entropy production (MEP) is one of the proposed principles and is subject of this study. It states that a steady state system is organized in such a way that entropy production is maximized. Although successful applications have been reported in...

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

NASA Astrophysics Data System (ADS)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

A quantitative method for evaluating inferior glenohumeral joint stiffness using ultrasonography.

PubMed

Tsai, Wen-Wei; Lee, Ming-Yih; Yeh, Wen-Lin; Cheng, Shih-Chung; Soon, Kok-Soon; Lei, Kin Fong; Lin, Wen-Yen

2013-02-01

Subluxation of the affected shoulder in post-stroke patients is associated with nerve disorders and muscle fatigue. Clinicians must be able to accurately and reliably measure inferior glenohumeral subluxation in patients to provide appropriate treatment. However, quantitative methods for evaluating the laxity and stiffness of the glenohumeral joint (GHJ) are still being developed. The aim of this study was to develop a new protocol for evaluating the laxity and stiffness of the inferior GHJ using ultrasonography under optimal testing conditions and to investigate changes in the GHJ from a commercially available humerus brace and shoulder brace. Multistage inferior displacement forces were applied to create a glide between the most cephalad point on the visible anterosuperior surface of the humeral head and coracoid process in seven healthy volunteers. GHJ stiffness was defined as the slope of the linear regression line between the glides and different testing loads. The testing conditions were defined by different test loading mechanisms (n=2), shoulder constraining conditions (n=2), and loading modes (n=4). The optimal testing condition was defined as the condition with the least residual variance of measured laxity to the calculated stiffness under different testing loads. A paired t-test was used to compare the laxity and stiffness of the inferior GHJ using different braces. No significant difference was identified between the two test loading mechanisms (t=0.218, p=0.831) and two shoulder constraining conditions (t=-0.235, p=0.818). We concluded that ultrasonographic laxity measurements performed using a pulley set loading mechanism was as reliable as direct loading. Additionally, constraining the unloaded shoulder was proposed due to the lower mean residual variance value. Moreover, pulling the elbow downward with loading on the upper arm was suggested, as pulling the elbow downward with the elbow flexed and loading on the forearm may overestimate stiffness and pain in the inferior GHJ at the loading point due to friction between the wide belt and skin. Furthermore, subjects wearing a humerus brace with a belt, which creates the effect of lifting the humerus toward the acromion, had greater GHJ stiffness compared to subjects wearing a shoulder brace without a belt to lift the humerus under the proposed testing conditions. This study provides experimental evidence that shoulder braces may reduce GHJ laxity under an external load, implying that the use of a humeral brace can prevent subluxation in post-stroke patients. The resulting optimal testing conditions for measuring the laxity and stiffness of the GHJ is to constrain the unloaded shoulder and bend the loaded arm at the elbow with loading on the upper arm using a pulley system. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

Resistance spot welding of ultra-fine grained steel sheets produced by constrained groove pressing: Optimization and characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khodabakhshi, F.; Kazeminezhad, M., E-mail: mkazemi@sharif.edu; Kokabi, A.H.

2012-07-15

Constrained groove pressing as a severe plastic deformation method is utilized to produce ultra-fine grained low carbon steel sheets. The ultra-fine grained sheets are joined via resistance spot welding process and the characteristics of spot welds are investigated. Resistance spot welding process is optimized for welding of the sheets with different severe deformations and their results are compared with those of as-received samples. The effects of failure mode and expulsion on the performance of ultra-fine grained sheet spot welds have been investigated in the present paper and the welding current and time of resistance spot welding process according to thesemore » subjects are optimized. Failure mode and failure load obtained in tensile-shear test, microhardness, X-ray diffraction, transmission electron microscope and scanning electron microscope images have been used to describe the performance of spot welds. The region between interfacial to pullout mode transition and expulsion limit is defined as the optimum welding condition. The results show that optimum welding parameters (welding current and welding time) for ultra-fine grained sheets are shifted to lower values with respect to those for as-received specimens. In ultra-fine grained sheets, one new region is formed named recrystallized zone in addition to fusion zone, heat affected zone and base metal. It is shown that microstructures of different zones in ultra-fine grained sheets are finer than those of as-received sheets. - Highlights: Black-Right-Pointing-Pointer Resistance spot welding process is optimized for joining of UFG steel sheets. Black-Right-Pointing-Pointer Optimum welding current and time are decreased with increasing the CGP pass number. Black-Right-Pointing-Pointer Microhardness at BM, HAZ, FZ and recrystallized zone is enhanced due to CGP.« less

Analytical investigations in aircraft and spacecraft trajectory optimization and optimal guidance

NASA Technical Reports Server (NTRS)

Markopoulos, Nikos; Calise, Anthony J.

1995-01-01

A collection of analytical studies is presented related to unconstrained and constrained aircraft (a/c) energy-state modeling and to spacecraft (s/c) motion under continuous thrust. With regard to a/c unconstrained energy-state modeling, the physical origin of the singular perturbation parameter that accounts for the observed 2-time-scale behavior of a/c during energy climbs is identified and explained. With regard to the constrained energy-state modeling, optimal control problems are studied involving active state-variable inequality constraints. Departing from the practical deficiencies of the control programs for such problems that result from the traditional formulations, a complete reformulation is proposed for these problems which, in contrast to the old formulation, will presumably lead to practically useful controllers that can track an inequality constraint boundary asymptotically, and even in the presence of 2-sided perturbations about it. Finally, with regard to s/c motion under continuous thrust, a thrust program is proposed for which the equations of 2-dimensional motion of a space vehicle in orbit, viewed as a point mass, afford an exact analytic solution. The thrust program arises under the assumption of tangential thrust from the costate system corresponding to minimum-fuel, power-limited, coplanar transfers between two arbitrary conics. The thrust program can be used not only with power-limited propulsion systems, but also with any propulsion system capable of generating continuous thrust of controllable magnitude, and, for propulsion types and classes of transfers for which it is sufficiently optimal the results of this report suggest a method of maneuvering during planetocentric or heliocentric orbital operations, requiring a minimum amount of computation; thus uniquely suitable for real-time feedback guidance implementations.

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

NASA Astrophysics Data System (ADS)

Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

2017-06-01

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Climate change in fish: effects of respiratory constraints on optimal life history and behaviour.

PubMed

Holt, Rebecca E; Jørgensen, Christian

2015-02-01

The difference between maximum metabolic rate and standard metabolic rate is referred to as aerobic scope, and because it constrains performance it is suggested to constitute a key limiting process prescribing how fish may cope with or adapt to climate warming. We use an evolutionary bioenergetics model for Atlantic cod (Gadus morhua) to predict optimal life histories and behaviours at different temperatures. The model assumes common trade-offs and predicts that optimal temperatures for growth and fitness lie below that for aerobic scope; aerobic scope is thus a poor predictor of fitness at high temperatures. Initially, warming expands aerobic scope, allowing for faster growth and increased reproduction. Beyond the optimal temperature for fitness, increased metabolic requirements intensify foraging and reduce survival; oxygen budgeting conflicts thus constrain successful completion of the life cycle. The model illustrates how physiological adaptations are part of a suite of traits that have coevolved. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A formulation and analysis of combat games

NASA Technical Reports Server (NTRS)

Heymann, M.; Ardema, M. D.; Rajan, N.

1984-01-01

Combat which is formulated as a dynamical encounter between two opponents, each of whom has offensive capabilities and objectives is outlined. A target set is associated with each opponent in the event space in which he endeavors to terminate the combat, thereby winning. If the combat terminates in both target sets simultaneously, or in neither, a joint capture or a draw, respectively, occurs. Resolution of the encounter is formulated as a combat game; as a pair of competing event constrained differential games. If exactly one of the players can win, the optimal strategies are determined from a resulting constrained zero sum differential game. Otherwise the optimal strategies are computed from a resulting nonzero sum game. Since optimal combat strategies may frequently not exist, approximate or delta combat games are also formulated leading to approximate or delta optimal strategies. The turret game is used to illustrate combat games. This game is sufficiently complex to exhibit a rich variety of combat behavior, much of which is not found in pursuit evasion games.

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

NASA Technical Reports Server (NTRS)

Giesy, D. P.

1978-01-01

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Fusion of Hard and Soft Information in Nonparametric Density Estimation

DTIC Science & Technology

2015-06-10

and stochastic optimization models, in analysis of simulation output, and when instantiating probability models. We adopt a constrained maximum...particular, density estimation is needed for generation of input densities to simulation and stochastic optimization models, in analysis of simulation output...an essential step in simulation analysis and stochastic optimization is the generation of probability densities for input random variables; see for

Distributed Constrained Optimization with Semicoordinate Transformations

NASA Technical Reports Server (NTRS)

Macready, William; Wolpert, David

2006-01-01

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve constrained optimization problems. This is done by translating the problem into an iterated game, where each agent controls a different variable of the problem, so that the joint probability distribution across the agents moves gives an expected value of the objective function. The dynamics of the agents is designed to minimize a Lagrangian function of that joint distribution. Here we illustrate how the updating of the Lagrange parameters in the Lagrangian is a form of automated annealing, which focuses the joint distribution more and more tightly about the joint moves that optimize the objective function. We then investigate the use of "semicoordinate" variable transformations. These separate the joint state of the agents from the variables of the optimization problem, with the two connected by an onto mapping. We present experiments illustrating the ability of such transformations to facilitate optimization. We focus on the special kind of transformation in which the statistically independent states of the agents induces a mixture distribution over the optimization variables. Computer experiment illustrate this for &sat constraint satisfaction problems and for unconstrained minimization of NK functions.

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Method of texturing a superconductive oxide precursor

DOEpatents

DeMoranville, Kenneth L.; Li, Qi; Antaya, Peter D.; Christopherson, Craig J.; Riley, Jr., Gilbert N.; Seuntjens, Jeffrey M.

1999-01-01

A method of forming a textured superconductor wire includes constraining an elongated superconductor precursor between two constraining elongated members placed in contact therewith on opposite sides of the superconductor precursor, and passing the superconductor precursor with the two constraining members through flat rolls to form the textured superconductor wire. The method includes selecting desired cross-sectional shape and size constraining members to control the width of the formed superconductor wire. A textured superconductor wire formed by the method of the invention has regular-shaped, curved sides and is free of flashing. A rolling assembly for single-pass rolling of the elongated precursor superconductor includes two rolls, two constraining members, and a fixture for feeding the precursor superconductor and the constraining members between the rolls. In alternate embodiments of the invention, the rolls can have machined regions which will contact only the elongated constraining members and affect the lateral deformation and movement of those members during the rolling process.

Continuous Shape Estimation of Continuum Robots Using X-ray Images

PubMed Central

Lobaton, Edgar J.; Fu, Jinghua; Torres, Luis G.; Alterovitz, Ron

2015-01-01

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot’s shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints. PMID:26279960

Continuous Shape Estimation of Continuum Robots Using X-ray Images.

PubMed

Lobaton, Edgar J; Fu, Jinghua; Torres, Luis G; Alterovitz, Ron

2013-05-06

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot's shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints.

Trajectory optimization for the National Aerospace Plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1993-01-01

The objective of this second phase research is to investigate the optimal ascent trajectory for the National Aerospace Plane (NASP) from runway take-off to orbital insertion and address the unique problems associated with the hypersonic flight trajectory optimization. The trajectory optimization problem for an aerospace plane is a highly challenging problem because of the complexity involved. Previous work has been successful in obtaining sub-optimal trajectories by using energy-state approximation and time-scale decomposition techniques. But it is known that the energy-state approximation is not valid in certain portions of the trajectory. This research aims at employing full dynamics of the aerospace plane and emphasizing direct trajectory optimization methods. The major accomplishments of this research include the first-time development of an inverse dynamics approach in trajectory optimization which enables us to generate optimal trajectories for the aerospace plane efficiently and reliably, and general analytical solutions to constrained hypersonic trajectories that has wide application in trajectory optimization as well as in guidance and flight dynamics. Optimal trajectories in abort landing and ascent augmented with rocket propulsion and thrust vectoring control were also investigated. Motivated by this study, a new global trajectory optimization tool using continuous simulated annealing and a nonlinear predictive feedback guidance law have been under investigation and some promising results have been obtained, which may well lead to more significant development and application in the near future.

Vibration control of beams using constrained layer damping with functionally graded viscoelastic cores: theory and experiments

NASA Astrophysics Data System (ADS)

El-Sabbagh, A.; Baz, A.

2006-03-01

Conventionally, the viscoelastic cores of Constrained Layer Damping (CLD) treatments are made of materials that have uniform shear modulus. Under such conditions, it is well-recognized that these treatments are only effective near their edges where the shear strains attain their highest values. In order to enhance the damping characteristics of the CLD treatments, we propose to manufacture the cores from Functionally Graded ViscoElastic Materials (FGVEM) that have optimally selected gradient of the shear modulus over the length of the treatments. With such optimized distribution of the shear modulus, the shear strain can be enhanced, and the energy dissipation can be maximized. The theory governing the vibration of beams treated with CLD, that has functionally graded viscoelastic cores, is presented using the finite element method (FEM). The predictions of the FEM are validated experimentally for plain beams, beams treated conventional CLD, and beams with CLD/FGVEM of different configurations. The obtained results indicate a close agreement between theory and experiments. Furthermore, the obtained results demonstrate the effectiveness of the new class of CLD with functionally graded cores in enhancing the energy dissipation over the conventional CLD over a broad frequency band. Extension of the proposed one-dimensional beam/CLD/FGVEM system to more complex structures is a natural extension to the present study.

Full-waveform inversion for the Iranian plateau

NASA Astrophysics Data System (ADS)

Masouminia, N.; Fichtner, A.; Rahimi, H.

2017-12-01

We aim to obtain a detailed tomographic model for the Iranian plateau facilitated by full-waveform inversion. By using this method, we intend to better constrain the 3-D structure of the crust and the upper mantle in the region. The Iranian plateau is a complex tectonic area resulting from the collision of the Arabian and Eurasian tectonic plates. This region is subject to complex tectonic processes such as Makran subduction zone, which runs along the southeastern coast of Iran, and the convergence of the Arabian and- Eurasian plates, which itself led to another subduction under Central Iran. This continent-continent collision has also caused shortening and crustal thickening, which can be seen today as Zagros mountain range in the south and Kopeh Dagh mountain range in the northeast. As a result of such a tectonic activity, the crust and the mantle beneath the region are expected to be highly heterogeneous. To further our understanding of the region and its tectonic history, a detailed 3-D velocity model is required.To construct a 3-D model, we propose to use full-waveform inversion, which allows us to incorporate all types of waves recorded in the seismogram, including body waves as well as fundamental- and higher-mode surface waves. Exploiting more information from the observed data using this approach is likely to constrain features which have not been found by classical tomography studies so far. We address the forward problem using Salvus - a numerical wave propagation solver, based on spectral-element method and run on high-performance computers. The solver allows us to simulate wave field propagating in highly heterogeneous, attenuating and anisotropic media, respecting the surface topography. To improve the model, we solve the optimization problem. Solution of this optimization problem is based on an iterative approach which employs adjoint methods to calculate the gradient and uses steepest descent and conjugate-gradient methods to minimize the objective function. Each iteration of such an approach is expected to bring the model closer to the true model.Our model domain extends between 25°N and 40°N in latitude and 42°E and 63°E in longitude. To constrain the 3-D structure of the area we use 83 broadband seismic stations and 146 earthquakes with magnitude Mw>4.5 -that occurred in the region between 2012 and 2017.

Optimality conditions for the numerical solution of optimization problems with PDE constraints :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

2014-03-01

A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

Fracture characterization from near-offset VSP inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Horne, S.; MacBeth, C.; Queen, J.

1997-01-01

A global optimization method incorporating a ray-tracing scheme is used to invert observations of shear-wave splitting from two near-offset VSPs recorded at the Conoco Borehole Test Facility, Kay County, Oklahoma. Inversion results suggest that the seismic anisotropy is due to a non-vertical fracture system. This interpretation is constrained by the VSP acquisition geometry for which two sources are employed along near diametrically opposite azimuths about the well heads. A correlation is noted between the time-delay variations between the fast and slow split shear waves and the sandstone formations.

A Toolbox for Imaging Stellar Surfaces

NASA Astrophysics Data System (ADS)

Young, John

2018-04-01

In this talk I will review the available algorithms for synthesis imaging at visible and infrared wavelengths, including both gray and polychromatic methods. I will explain state-of-the-art approaches to constraining the ill-posed image reconstruction problem, and selecting an appropriate regularisation function and strength of regularisation. The reconstruction biases that can follow from non-optimal choices will be discussed, including their potential impact on the physical interpretation of the results. This discussion will be illustrated with example stellar surface imaging results from real VLTI and COAST datasets.

Variability in Tropospheric Ozone over China Derived from Assimilated GOME-2 Ozone Profiles

NASA Astrophysics Data System (ADS)

van Peet, J. C. A.; van der A, R. J.; Kelder, H. M.

2016-08-01

A tropospheric ozone dataset is derived from assimilated GOME-2 ozone profiles for 2008. Ozone profiles are retrieved with the OPERA algorithm, using the optimal estimation method. The retrievals are done on a spatial resolution of 160×160 km on 16 layers ranging from the surface up to 0.01 hPa. By using the averaging kernels in the data assimilation, the algorithm maintains the high resolution vertical structures of the model, while being constrained by observations with a lower vertical resolution.

Benchmarking optimization software with COPS 3.0.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolan, E. D.; More, J. J.; Munson, T. S.

2004-05-24

The authors describe version 3.0 of the COPS set of nonlinearly constrained optimization problems. They have added new problems, as well as streamlined and improved most of the problems. They also provide a comparison of the FILTER, KNITRO, LOQO, MINOS, and SNOPT solvers on these problems.

Evaluating data worth for ground-water management under uncertainty

USGS Publications Warehouse

Wagner, B.J.

1999-01-01

A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models-a chance-constrained ground-water management model and an integer-programing sampling network design model-to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information-i.e., the projected reduction in management costs-with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models - a chance-constrained ground-water management model and an integer-programming sampling network design model - to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information - i.e., the projected reduction in management costs - with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

Photon-efficient super-resolution laser radar

NASA Astrophysics Data System (ADS)

Shin, Dongeek; Shapiro, Jeffrey H.; Goyal, Vivek K.

2017-08-01

The resolution achieved in photon-efficient active optical range imaging systems can be low due to non-idealities such as propagation through a diffuse scattering medium. We propose a constrained optimization-based frame- work to address extremes in scarcity of photons and blurring by a forward imaging kernel. We provide two algorithms for the resulting inverse problem: a greedy algorithm, inspired by sparse pursuit algorithms; and a convex optimization heuristic that incorporates image total variation regularization. We demonstrate that our framework outperforms existing deconvolution imaging techniques in terms of peak signal-to-noise ratio. Since our proposed method is able to super-resolve depth features using small numbers of photon counts, it can be useful for observing fine-scale phenomena in remote sensing through a scattering medium and through-the-skin biomedical imaging applications.

Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.

PubMed

Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

2017-09-01

In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pareto-optimal reversed-phase chromatography separation of three insulin variants with a solubility constraint.

PubMed

Arkell, Karolina; Knutson, Hans-Kristian; Frederiksen, Søren S; Breil, Martin P; Nilsson, Bernt

2018-01-12

With the shift of focus of the regulatory bodies, from fixed process conditions towards flexible ones based on process understanding, model-based optimization is becoming an important tool for process development within the biopharmaceutical industry. In this paper, a multi-objective optimization study of separation of three insulin variants by reversed-phase chromatography (RPC) is presented. The decision variables were the load factor, the concentrations of ethanol and KCl in the eluent, and the cut points for the product pooling. In addition to the purity constraints, a solubility constraint on the total insulin concentration was applied. The insulin solubility is a function of the ethanol concentration in the mobile phase, and the main aim was to investigate the effect of this constraint on the maximal productivity. Multi-objective optimization was performed with and without the solubility constraint, and visualized as Pareto fronts, showing the optimal combinations of the two objectives productivity and yield for each case. Comparison of the constrained and unconstrained Pareto fronts showed that the former diverges when the constraint becomes active, because the increase in productivity with decreasing yield is almost halted. Consequently, we suggest the operating point at which the total outlet concentration of insulin reaches the solubility limit as the most suitable one. According to the results from the constrained optimizations, the maximal productivity on the C 4 adsorbent (0.41 kg/(m 3  column h)) is less than half of that on the C 18 adsorbent (0.87 kg/(m 3  column h)). This is partly caused by the higher selectivity between the insulin variants on the C 18 adsorbent, but the main reason is the difference in how the solubility constraint affects the processes. Since the optimal ethanol concentration for elution on the C 18 adsorbent is higher than for the C 4 one, the insulin solubility is also higher, allowing a higher pool concentration. An alternative method of finding the suggested operating point was also evaluated, and it was shown to give very satisfactory results for well-mapped Pareto fronts. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid Slewing of Flexible Space Structures

DTIC Science & Technology

2015-09-01

axis gimbal with elastic joints. The performance of the system can be enhanced by designing antenna maneuvers in which the flexible effects are...the effects of the nonlinearities so the vibrational motion can be constrained for a time-optimal slew. It is shown that by constructing an...joints. The performance of the system can be enhanced by designing antenna maneuvers in which the flexible effects are properly constrained, thus

A robust, efficient equidistribution 2D grid generation method

NASA Astrophysics Data System (ADS)

Chacon, Luis; Delzanno, Gian Luca; Finn, John; Chung, Jeojin; Lapenta, Giovanni

2007-11-01

We present a new cell-area equidistribution method for two- dimensional grid adaptation [1]. The method is able to satisfy the equidistribution constraint to arbitrary precision while optimizing desired grid properties (such as isotropy and smoothness). The method is based on the minimization of the grid smoothness integral, constrained to producing a given positive-definite cell volume distribution. The procedure gives rise to a single, non-linear scalar equation with no free-parameters. We solve this equation numerically with the Newton-Krylov technique. The ellipticity property of the linearized scalar equation allows multigrid preconditioning techniques to be effectively used. We demonstrate a solution exists and is unique. Therefore, once the solution is found, the adapted grid cannot be folded due to the positivity of the constraint on the cell volumes. We present several challenging tests to show that our new method produces optimal grids in which the constraint is satisfied numerically to arbitrary precision. We also compare the new method to the deformation method [2] and show that our new method produces better quality grids. [1] G.L. Delzanno, L. Chac'on, J.M. Finn, Y. Chung, G. Lapenta, A new, robust equidistribution method for two-dimensional grid generation, in preparation. [2] G. Liao and D. Anderson, A new approach to grid generation, Appl. Anal. 44, 285--297 (1992).