Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems. PMID:22122384
Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.
2015-01-01
Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881
On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its
Ralphs, Ted
On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its University, USA COR@L Technical Report 14T-004 #12;On the Value Function of a Mixed Integer Linear the value function of a general mixed integer linear optimization prob- lem (MILP). The value function
Mitchell, John E.
Using MINTO on RCS MINTO stands for Mixed INTeger Optimizer. It is a package which solves mixed of being integrated into the RCS environment. This is an ongoing process but you can access MINTO right now. MINTO is available on fourteen selected RCS workstation by simply following these directions
NASA Astrophysics Data System (ADS)
Wang, Bin; Chiang, Hsiao-Dong
Many applications of smart grid can be formulated as constrained optimization problems. Because of the discrete controls involved in power systems, these problems are essentially mixed-integer nonlinear programs. In this paper, we review the Trust-Tech-based methodology for solving mixed-integer nonlinear optimization. Specifically, we have developed a two-stage Trust-Tech-based methodology to systematically compute all the local optimal solutions for constrained mixed-integer nonlinear programming (MINLP) problems. In the first stage, for a given MINLP problem this methodology starts with the construction of a new, continuous, unconstrained problem through relaxation and the penalty function method. A corresponding dynamical system is then constructed to search for a set of local optimal solutions for the unconstrained problem. In the second stage, a reduced constrained NLP is defined for each local optimal solution by determining and fixing the values of integral variables of the MINLP problem. The Trust-Tech-based method is used to compute a set of local optimal solutions for these reduced NLP problems, from which the optimal solution of the original MINLP problem is determined. A numerical simulation of several testing problems is provided to illustrate the effectiveness of our proposed method.
Computational experiments for local search algorithms for binary and mixed integer optimization
Zhou, Jingting, S.M. Massachusetts Institute of Technology
2010-01-01
In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...
Trajectory Optimization using Mixed-Integer Linear Programming
to UAVs and the assignment of spacecraft to positions in a formation. These requirements lead to non (MILP) that can be solved for global optimality using powerful, commercial software. This thesis optimization, and also to include a general form of waypoint as- signment suitable for UAV problems. Finally
Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach
NASA Astrophysics Data System (ADS)
Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin
2014-09-01
Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.
A Mixed-Integer Optimization Framework for De Novo Peptide Identification
DiMaggio, Peter A.
2009-01-01
A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. PMID:19412358
Optimization of a wood dryer kiln using the mixed integer programming technique: A case study
Gustafsson, S.I.
1999-07-01
When wood is to be utilized as a raw material for furniture, buildings, etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination of monitored values from an actual drying kiln it has been possible to optimize the use of steam and electricity using the so called mixed integer programming technique. Owing to the operating schedule for the drying kiln it has been necessary to divide the drying process in very short time intervals, i.e., a number of minutes. Since a drying cycle takes about two or three weeks, a considerable mathematical problem is presented and this has to be solved.
NASA Astrophysics Data System (ADS)
Wang, Chuang; Dai, Jianrong; Hu, Yimin
2003-12-01
An algorithm for optimizing beam orientations and beam weights for conformal radiotherapy has been developed. The algorithm models the optimization of beam orientations and beam weights as a problem of mixed integer linear programming (MILP), and optimizes the beam orientations and beam weights simultaneously. The application process of the algorithm has four steps: (a) prepare a pool of beam orientation candidates with the consideration of avoiding any patient-gantry collision and avoiding direct irradiation of organs at risk with quite low tolerances (e.g., eyes). (b) Represent each beam orientation candidate with a binary variable, and each beam weight with a continuous variable. (c) Set up an optimization problem according to dose prescriptions and the maximum allowed number of beam orientations. (d) Solve the optimization problem with a ready-to-use MILP solver. After optimization, the candidates with unity binary variables remain in the final beam configuration. The performance of the algorithm was tested with clinical cases. Compared with standard treatment plans, the beam-orientation-optimized plans had better dose distributions in terms of target coverage and avoidance of critical structures. The optimization processes took less than 1 h on a PC with a Pentium IV 2.4 GHz processor.
Yang, Ruijie; Dai, Jianrong; Yang, Yong; Hu, Yimin
2006-08-01
The purpose of this study is to extend an algorithm proposed for beam orientation optimization in classical conformal radiotherapy to intensity-modulated radiation therapy (IMRT) and to evaluate the algorithm's performance in IMRT scenarios. In addition, the effect of the candidate pool of beam orientations, in terms of beam orientation resolution and starting orientation, on the optimized beam configuration, plan quality and optimization time is also explored. The algorithm is based on the technique of mixed integer linear programming in which binary and positive float variables are employed to represent candidates for beam orientation and beamlet weights in beam intensity maps. Both beam orientations and beam intensity maps are simultaneously optimized in the algorithm with a deterministic method. Several different clinical cases were used to test the algorithm and the results show that both target coverage and critical structures sparing were significantly improved for the plans with optimized beam orientations compared to those with equi-spaced beam orientations. The calculation time was less than an hour for the cases with 36 binary variables on a PC with a Pentium IV 2.66 GHz processor. It is also found that decreasing beam orientation resolution to 10 degrees greatly reduced the size of the candidate pool of beam orientations without significant influence on the optimized beam configuration and plan quality, while selecting different starting orientations had large influence. Our study demonstrates that the algorithm can be applied to IMRT scenarios, and better beam orientation configurations can be obtained using this algorithm. Furthermore, the optimization efficiency can be greatly increased through proper selection of beam orientation resolution and starting beam orientation while guaranteeing the optimized beam configurations and plan quality. PMID:16861772
2013-01-01
Background The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the ‘optimal’ hospital infrastructure would be in the Dutch context. Methods To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. Results The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). Conclusions In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a ‘what-if’ analysis. PMID:23768234
NASA Astrophysics Data System (ADS)
Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang
2008-12-01
Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.
Mixed integer programming model for optimizing the layout of an ICU vehicle
2009-01-01
Background This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). Methods The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. Results As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. Conclusion The outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. The authors advocate this approach to address similar problems within the field of Health Services to improve the efficiency and the effectiveness of the processes involved. Problems such as those in operation rooms or emergency rooms, where the availability of a large amount of material is critical are eligible to be dealt with in a simmilar manner. PMID:19995438
Optimal regional siting of power plants: a mixed-integer programming approach
Shoukri-Kourdi, F.
1988-01-01
The power-plant siting problem is a problem of determining what types and sizes of plant to build, what regional resources to exploit, where to build plants, when to build them, and what energy links are needed between resources and demand sites. Spatial decisions such as plant location and investment in long-distance, high-tension transmission lines, as well as fuel-transportation links, are explicitly considered. The model developed here is approximately applied to a smaller developing country or a particular region of a larger developing country. The problem is formulated in terms of mixed-integer programming (MIP) and a commercially available package, The Sunset Software, is used for solving the mixed-integer programming on a micro computer, the IBM XT. Two basic models are compared here, a linear programming (LP) model and an MIP model, and the results obtained illustrate the distinct advantages of using the MIP for problem formulation as well as the effects of considering different fuel transports along with transmission lines.
Optimal Shipboard Power System Management via Mixed Integer Dynamic Programming
Kwatny, Harry G.
feedback controls is described. Examples are given. I. INTRODUCTION Maintaining power flow to vital loads include a classical ordinary differ- ential equation (ODE) or differential-algebraic equation (DAE) model model is a convenient theoretical tool, other forms of models are far more convenient for control system
Optimizing well-stimulation treatment size using mixed integer linear programming
Picon Aranguren, Oscar
2002-01-01
This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...
Mixed Integer Programming Computation
Andrea Lodi
\\u000a The first 50 years of Integer and Mixed-Integer Programming have taken us to a very stable paradigm for solving problems in\\u000a a reliable and effective way. We run over these 50 exciting years by showing some crucial milestones and we highlight the\\u000a building blocks that are making nowadays solvers effective from both a performance and an application viewpoint. Finally,\\u000a we
Online trajectory planning for UAVs using mixed integer linear programming
Culligan, Kieran Forbes
2006-01-01
This thesis presents a improved path planner using mixed-integer linear programming (MILP) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAV's). Using MILP, hard constraints for obstacle ...
Polyhedral Approaches to Mixed Integer Linear Programming
Cornuejols, Gerard P.
, polyhedron, union of polyhedra, cutting plane, split cut, Gomory mixed integer cut, Chvâ??atal rank 1 programming. It applies them to the study of valid inequalities for mixed integer linear sets, such as Gomory's mixed integer cuts. Key words: mixed integer linear program, Farkas' lemma, MinkowskiÂWeyl theorem
Polyhedral Approaches to Mixed Integer Linear Programming
Cornuejols, Gerard P.
of polyhedra, cutting plane, split cut, Gomory mixed integer cut, ChvÂ´atal rank 1 Introduction 1.1 Mixed to the study of valid inequalities for mixed integer linear sets, such as Gomory's mixed integer cuts. KeyPolyhedral Approaches to Mixed Integer Linear Programming Michele Conforti Universit`a di Padova
Cutting Planes for Mixed Integer Programming
Michael Russell
The purpose of this paper is to present an overview of families of cutting planes for mixed integer programming problems. We examine the families of disjunctive inequalities, split cuts, mixed integer rounding inequalities, mixed integer Gomory cuts, intersection cuts, lift-and-project cuts, and reduce- and-split cuts. In practice, mixed integer Gomory cuts are very useful in obtaining solutions to mixed integer
Mixed-integer quadratic programming
Rafael Lazimy
1982-01-01
This paper considers mixed-integer quadratic programs in which the objective function is quadratic in the integer and in the continuous variables, and the constraints are linear in the variables of both types. The generalized Benders' decomposition is a suitable approach for solving such programs. However, the program does not become more tractable if this method is used, since Benders' cuts
Marc Jüdes; Stefan Vigerske; George Tsatsaronis
This paper focuses on the optimization of the design and operation of combined heat and power plants (cogeneration plants).\\u000a Due to the complexity of such an optimization task, conventional optimization methods consider only one operation point that\\u000a is usually the full-load case. However, the frequent changes in demand lead to operation in several partial-load conditions.\\u000a To guarantee a technically feasible
Abhishek, K.; Leyffer, S.; Linderoth, J. T.; Mathematics and Computer Science; Lehigh Univ.
2010-06-01
Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques, and we illustrate this approach on a load-bearing thermal insulation system. The system consists of a number of insulators of different materials and intercepts that minimize the heat flow from a hot surface to a cold surface. Our new model allows us to employ black-box modeling languages and solvers and illustrates the interplay between integer and nonlinear modeling techniques. We present numerical experience that illustrates the advantage of the standard integer model.
Numerically Accurate Gomory Mixed-Integer Cuts
William Cook; Ricardo Fukasawa; Marcos Goycoolea
2008-01-01
We describe a simple process for generating numerically accurate cutting planes using floating-point arithmetic and the mixed-integer rounding (MIR) procedure. Applying this method to the rows of the simplex tableau permits the generation of Gomory mixed- integer cuts that are guaranteed to be satisfied by all feasible solutions to a mixed-integer programming problem. We report on tests with the MIPLIB
Numerically Safe Gomory Mixed-Integer Cuts
William Cook; Sanjeeb Dash; Ricardo Fukasawa; Marcos Goycoolea
2009-01-01
We describe a simple process for generating numerically safe cutting planes using floating-point arithmetic and the mixed-integer rounding (MIR) procedure. Apply- ing this method to the rows of the simplex tableau permits the generation of Gomory mixed-integer cuts that are guaranteed to be satisfied by all feasible solutions to a mixed-integer programming problem. We report on tests with the MIPLIB
The Value Function of a Mixed-Integer Linear Program with a Single Constraint
Ralphs, Ted
The Value Function of a Mixed-Integer Linear Program with a Single Constraint M. G¨uzelsoy T. K. Ralphs March 27, 2008 Abstract The value function of a mixed-integer linear program (MILP) is a function that returns the optimal solution value as a function of the right-hand side. In this paper, we analyze
Mixed Integer Linear Programming Formulation Techniques
Vielma, Juan Pablo
A wide range of problems can be modeled as Mixed Integer Linear Programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effectively ...
convex segmentation and mixed-integer footstep planning for a walking robot
Deits, Robin L. H. (Robin Lloyd Henderson)
2014-01-01
This work presents a novel formulation of the footstep planning problem as a mixed-integer convex optimization. The footstep planning problem involves choosing a set of footstep locations which a walking robot can follow ...
Strengthening Gomory Mixed-Integer Cuts: A Computational Study
Franz Wesselmann
2009-01-01
Gomory mixed-integer cuts are an important ingredient in state-of- the-art software for solving mixed-integer linear programs. In particu- lar, much attention has been paid to the strengthening of these cuts. In this paper, we give an overview of existing approaches for improv- ing the performance of Gomory mixed-integer cuts. More precisely, we consider k-cuts, combined Gomory mixed-integer cuts, reduce-and-split cuts,
Finite Disjunctive Programming Characterizations for General Mixed-Integer Linear Programs
Binyuan Chen; Simge Küçükyavuz; Suvrajeet Sen
2011-01-01
In this paper, we give a nite disjunctive programming procedure to obtain the convex hull of general mixed-integer linear programs (MILP) with bounded integer variables. We propose a nitely convergent convex hull tree algorithm which constructs a linear program that has the same optimal solution as the associated MILP. In addition, we combine the standard notion of sequential cutting planes
Solid Waste Management System Analysis by Multiobjective Mixed Integer Programming Model
Ni-Bin Chang; S. F. Wang
1996-01-01
The conflict between economic optimization and environmental protection has received wide attention in recent research programs for solid waste management system planning. The purpose of this analysis is to apply multiobjective mixed integer programming techniques for reasoning the potential conflict between environmental and economic goals and for evaluating sustainable strategies for waste management in a metropolitan region. The information incorporated
Local cuts for mixed-integer programming Vasek Chvatal
Cook, William
a cutting plane in the original space. We apply this procedure to general mixed-integer programming problems to the linear programming (LP) problem max(cT x : x PLP). The cutting-plane method is combined with a branch] efficiently produces a cutting plane whenever x / PIP. Indeed, the Gomory mixed-integer (GMI) cuts found
Effective Separation of Disjunctive Cuts for Convex Mixed Integer Nonlinear Programs
Linderoth, Jeffrey T.
generation of disjunctive cutting planes for convex mixed-integer nonlinear programs. A mixed integer;Effective Separation of Disjunctive Cuts for Convex Mixed Integer Nonlinear Programs Mustafa KilinÂ¸c Jeff inequalities for convex mixed-integer nonlinear programs (MINLPs). The method relies on solving a sequence
Coefficient strengthening: a tool for reformulating mixed-integer programs
Kent Andersen; Yves Pochet
2010-01-01
Providing a good formulation is an important part of solving a mixed integer program. We suggest to measure the quality of a formulation by whether it is possible to strengthen the coef- flcients of the formulation. Sequentially strengthening coe-cients can then be used as a tool for improving formulations. We believe this method could be useful for analyzing and producing
Solving Mixed Integer Linear Programs Using Branch and Cut Algorithm
Sivaramakrishnan, Kartik K.
1 Solving Mixed Integer Linear Programs Using Branch and Cut Algorithm by Shon Albert A Project and Bound Scheme and Gomory Cutting Planes Scheme. Branch and Cut should be faster than Branch and Bound+ . One method is involves using Cutting Planes. The goal of cutting planes is to "cut" out a chunk
ALGORITHMS AND SOFTWARE FOR CONVEX MIXED INTEGER NONLINEAR PROGRAMS
PIERRE BONAMI; MUSTAFA KILINC; JEFF LINDEROTH
This paper provides a survey of recent progress and software for solving mixed integer nonlinear programs (MINLP) wherein the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in very years. By exploiting analogies to the case of well-known techniques for solving mixed
Duality for Mixed-Integer Linear Programs M. Guzelsoy
Ralphs, Ted
, 2007 Abstract The theory of duality for linear programs is well-developed and has been successful for linear programs (LPs) is well-developed and has been extremely successful in contributing to both theoryDuality for Mixed-Integer Linear Programs M. Guzelsoy T. K. Ralphs Original May, 2006 Revised April
The SYMPHONY Callable Library for Mixed Integer Programming Menal Guzelsoy
Ralphs, Ted
-OR is to promote the development of open source software for operations research. The COIN-OR software repository, 2004 Abstract SYMPHONY is a customizable, open-source library for solving mixed-integer linear pro currently hosts a dozen open source projects, all available for free download. SYMPHONY is an open-source
Parametric Mixed Integer Programming: An Application to Solid Waste Management
Larry Jenkins
1982-01-01
A method is developed for carrying out parametric analysis on a mixed integer linear program (MILP) as either objective function coefficients or right-hand-side values of the constraints are varied continuously. The method involves solving MILPs at point values of the parameters of variation and joining the results by LP parametric analysis. The procedure for parametric analysis on the objective function
K-Cuts: A Variation of Gomory Mixed Integer Cuts from the LP Tableau
Cornuejols, Gerard P.
@akula.isye.gatech.edu ||||||||||||||||||||||||||||{ For an integer program, a k-cut is a cutting plane generated by the Gomory mixed integer procedure from a row are reported to illustrate this property. (Integer Programming; LP Tableau; Gomory Mixed Integer Cut; K-cut cutting planes, fractional cuts and mixed integer cuts [9, 10, 11], for solving 1 #12; integer programming
Lift-and-Project Cuts for Mixed Integer Convex Programs
Pierre Bonami
\\u000a This paper addresses the problem of generating cuts for mixed integer nonlinear programs where the objective is linear and\\u000a the relations between the decision variables are described by convex functions defining a convex feasible region. We propose\\u000a a new method for strengthening the continuous relaxations of such problems using cutting planes. Our method can be seen as\\u000a a practical implementation
Semi-Continuous Cuts for Mixed-Integer Programming
I. R. de Farias JR
We study the convex hull of the feasible set of the semi-continuous knapsack prob- lem, in which the variables belong to the union of two intervals. Besides being im- portant in its own right, the semi-continuous knapsack problem is a relaxation of general mixed-integer programming. We show how strong inequalities valid for the semi-continuous knapsack polyhedron can be derived and
Mixed-integer programming applied to short-term planning of a hydro-thermal system
Nilsson, O. [Royal Inst. of Tech., Stockholm (Sweden). Dept. of Electric Power Engineering] [Royal Inst. of Tech., Stockholm (Sweden). Dept. of Electric Power Engineering; Sjelvgren, D. [Vattenfall AB, Stockholm (Sweden)] [Vattenfall AB, Stockholm (Sweden)
1996-02-01
In this paper a mixed-integer hydro electric power model for short-term planing is presented. The advantage of this model is that the schedules only include points with good efficiency. The planning problem is decomposed into a subproblem for each hydro plant. In order to get smooth schedules the model includes start-up costs for hydro aggregates. The main mathematical methods used in this work are Lagrange relaxation, dynamic programming and network programming. The model is illustrated by a numerical example from a part of the Swedish power system.
Mixed-integer programming applied to short-term planning of a hydro-thermal system
Nilsson, O. [Royal Inst. of Tech., Stockholm (Sweden). Dept. of Electric Power Engineering; Sjelvgren, D. [Vattenfall AB, Stockholm (Sweden)
1995-12-31
In this paper a mixed-integer hydro electric power model for short-term planning is presented. The advantage of this model is that the schedules only include points with good efficiency. The planning problem is decomposed into a subproblem for each hydro plant. In order to get smooth schedules the model includes start-up costs for hydro aggregates. The main mathematical methods used in this work are Lagrange relaxation, dynamic programming and network programming. The model is illustrated by a numerical example from a part of the Swedish power system.
PySP : modeling and solving stochastic mixed-integer programs in Python.
Woodruff, David L. (University of California, Davis); Watson, Jean-Paul
2010-08-01
Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.
An Exact Rational Mixed-Integer Programming Solver
William Cook; Thorsten Koch; Daniel Steffy; Kati Wolter
\\u000a We present an exact rational solver for mixed-integer linear programming that avoids the numerical inaccuracies inherent in\\u000a the floating-point computations used by existing software. This allows the solver to be used for establishing theoretical\\u000a results and in applications where correct solutions are critical due to legal and financial consequences. Our solver is a\\u000a hybrid symbolic\\/numeric implementation of LP-based branch-and-bound, using
Linderoth, Jeff T. [University of Wisconsin-Madison] [University of Wisconsin-Madison; Luedtke, James R. [University of Wisconsin-Madison] [University of Wisconsin-Madison
2013-05-30
The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.
Strong cutting planes for unstructured mixed integer programs using multiple constraints
Santanu S Dey
2007-01-01
In this thesis, we develop efficient methods to generate cutting planes for unstructured mixed integer programs using the information contained in several constraints simultaneously. To this end, we extend group and lifting approaches for cutting plane generation to mixed integer programs with multiple constraints. ^ First we study multi-dimensional group cuts. We derive properties of extreme valid functions for infinite
ERIC Educational Resources Information Center
Han, Kyung T.; Rudner, Lawrence M.
2014-01-01
This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…
Learning oncogenetic networks by reducing to mixed integer linear programming.
Shahrabi Farahani, Hossein; Lagergren, Jens
2013-01-01
Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog. PMID:23799047
NASA Astrophysics Data System (ADS)
Onoyama, Takashi; Kubota, Sen; Maekawa, Takuya; Komoda, Norihisa
Adequate response performance is required for the planning of a cooperative logistic network covering multiple enterprises, because this process needs a human expert's evaluation from many aspects. To satisfy this requirement, we propose an accurate model based on mixed integer programming for optimizing cooperative logistics networks where “round transportation” exists together with “depot transportation” including lower limit constraints of loading ratio for round transportation vehicles. Furthermore, to achieve interactive response performance, a dummy load is introduced into the model instead of integer variables. The experimental result shows the proposed method obtains an accurate solution within interactive response time.
An Interval Mixed-Integer Semi-Infinite Programming Method for Municipal Solid Waste Management
Li He; Guo H. Huang; Guangming Zeng; Hongwei Lu; John Koupal; Fred Minassian; Hannah Murray; Mani Natarajan; Fenfen Zhu; Masaki Takaoka; Kazuyuki Oshita; Shinsuke Morisawa; Hiroshi Tsuno; Yoshinori Kitajima; Wan-Fu Chiang; Hung-Yuan Fang; Chao-Hsiung Wu; Chang-Jun Huang; Ching-Yuan Chang; Yu-Min Chang; Ching-Liang Chen; Anders Nielsen; Lars Nielsen; Anders Feilberg; Knud Christensen; Yu-Yin Liu; Ta-Chang Lin; Ying-Jan Wang; Wei-Lun Ho; Janet Yanowitz; Robert McCormick; Lei Yu; Shichen Jia; Qinyi Shi; Tsang-Jung Chang; Hong-Ming Kao; Yu-Ting Wu; Wei-Hua Huang; Patrick Goodman; David Rich; Ariana Zeka; Luke Clancy; Douglas Dockery; Thomas Lavery; Christopher Rogers; Ralph Baumgardner; Kevin Mishoe; Wei-Chin Chen; Hsun-Yu Lin; Chung-Shin Yuan; Chung-Hsuang Hung; Guo Huang
2009-01-01
This study proposed an interval mixed-integer semi-infinite programming (IMISIP) method for solid waste management under uncertainty. The uncertainty can be expressed as various constants, intervals, and functional intervals. The method is mainly based on the previous efforts on interval mixed-integer linear programming (IMILP) and semi-infinite programming. The method is applied to a solid-waste management system to illustrate its effectiveness in
A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem
NASA Technical Reports Server (NTRS)
Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad
2010-01-01
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
A Two-Stage Stochastic Mixed-Integer Programming Approach to the Smart House Scheduling Problem
NASA Astrophysics Data System (ADS)
Ozoe, Shunsuke; Tanaka, Yoichi; Fukushima, Masao
A “Smart House” is a highly energy-optimized house equipped with photovoltaic systems (PV systems), electric battery systems, fuel cell cogeneration systems (FC systems), electric vehicles (EVs) and so on. Smart houses are attracting much attention recently thanks to their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC system and an EV. We formulate the problem as a mixed integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method.
Synchronic interval Gaussian mixed-integer programming for air quality management.
Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong
2015-12-15
To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM. PMID:26367068
Solution of Mixed-Integer Programming Problems on the XT5
Hartman-Baker, Rebecca J [ORNL; Busch, Ingrid Karin [ORNL; Hilliard, Michael R [ORNL; Middleton, Richard S [ORNL; Schultze, Michael [ORNL
2009-01-01
In this paper, we describe our experience with solving difficult mixed-integer linear programming problems (MILPs) on the petaflop Cray XT5 system at the National Center for Computational Sciences at Oak Ridge National Laboratory. We describe the algorithmic, software, and hardware needs for solving MILPs and present the results of using PICO, an open-source, parallel, mixed-integer linear programming solver developed at Sandia National Laboratories, to solve canonical MILPs as well as problems of interest arising from the logistics and supply chain management field.
Guo, P., E-mail: guoping@iseis.or [College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083 (China); Huang, G.H., E-mail: gordon.huang@uregina.c [Environmental Systems Engineering Program, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban and Environmental Sciences, Peking University, Beijing, 100871 (China)
2010-03-15
In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context.
General Mixed Integer Programming: Computational Issues for Branch-and-Cut Algorithms
Alexander Martin
2001-01-01
Abstract. In this paper we survey the basic features of state-of-the-art branch-and-cut algorithms for the solution of general mixed integer pro- gramming problems. In particular we focus on preprocessing techniques, branch-and-bound issues and cutting plane generation.
Duality for MixedInteger Linear Programs M. Guzelsoy # T. K. Ralphs +
Ralphs, Ted
April, 2007 Abstract The theory of duality for linear programs is welldeveloped and has been successful for linear programs (LPs) is welldeveloped and has been extremely successful in contributing to both theoryDuality for MixedInteger Linear Programs M. Guzelsoy # T. K. Ralphs + Original May, 2006 Revised
The SYMPHONY Callable Library for Mixed Integer Programming Ted Ralphs Menal Guzelsoy y
Ralphs, Ted
goal of COIN-OR is to promote the development of open source software for operations research. The COIN-OR software repository currently hosts a dozen open source projects, all available for free download. SYMPHONY 19, 2004 Abstract SYMPHONY is a customizable, open-source library for solving mixed-integer linear
Amending and enhancing electoral laws through mixed integer programming: the case of Italy
Serafini, Paolo
Amending and enhancing electoral laws through mixed integer programming: the case of Italy Aline Pennisi 1 , Federica Ricca 1 , Paolo Serafini 2 , Bruno Simeone 3 1 Electoral Systems expert, Rome, Italy 2 Dept. of Mathematics and Computer Science, University of Udine, Italy 3 Dept. of Statistics, La
Masatoshi Sakawa; Kosuke Kato; Satoshi Ushiro; Mare Inaoka
2001-01-01
In recent years, an operation planning of a district heating and cooling (DHC) plant has been arousing interest as a result of development of cooling load or heat demand prediction methods for district heating and cooling systems. In this paper, we formulate an operation planning of a district heating and cooling plant as a mixed integer linear programming problem. Since
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY programming (MINLP) formulation to achieve mini- mum-cost designs for reinforced concrete (RC) structures applications, specifically, in the context of the design of reinforced concrete (RC) structures. RC possesses
M. E. M. B. Gaid; A. Cela; Y. Hamam
2006-01-01
This brief addresses the problem of the optimal control and scheduling of networked control systems over limited bandwidth deterministic networks. Multivariable linear systems subject to communication constraints are modeled in the mixed logical dynamical (MLD) framework. The translation of the MLD model into the mixed integer quadratic programming (MIQP) formulation is described. This formulation allows the solving of the optimal
Combinatorial therapy discovery using mixed integer linear programming
Pang, Kaifang; Wan, Ying-Wooi; Choi, William T.; Donehower, Lawrence A.; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong
2014-01-01
Motivation: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Results: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Availability: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. Contact: zhandong.liu@bcm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24463180
A Simplex-Based Algorithm for 0-1 Mixed Integer Programming
Jean-philippe P. Richard; Ismael R. de Farias Jr.; George L. Nemhauser
2001-01-01
We present a finitely convergent cutting plane algorithm for 0-1 mixed integer programming. The algorithm is a hybrid between\\u000a a strong cutting plane and a Gomory-type algorithm that generates violated facet-defining inequalities of a relaxation of\\u000a the simplex tableau and uses them as cuts for the original problem. We show that the cuts can be computed in polynomial time\\u000a and
Disjunctive Cuts for Non-convex Mixed Integer Quadratically Constrained Programs
Anureet Saxena; Pierre Bonami; Jon Lee
2008-01-01
This paper addresses the problem of generating strong convex relaxations of Mixed Integer Quadratically Constrained Programming\\u000a (MIQCP) problems. MIQCP problems are very difficult because they combine two kinds of non-convexities: integer variables and\\u000a non-convex quadratic constraints. To produce strong relaxations of MIQCP problems, we use techniques from disjunctive programming\\u000a and the lift-and-project methodology. In particular, we propose new methods for
DRIESSEN,BRIAN; SADEGH,NADER
2000-04-25
This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.
NASA Astrophysics Data System (ADS)
Ziari, Houshmand A.; McCarl, Bruce A.; Stockle, Claudio
1995-06-01
On-farm runoff collection through small impoundments (ponds) is a potential irrigation water source. This study evaluates the economic feasibility of such impoundments for supplemental irrigation in the Blacklands region of Texas. This is done using a risk sensitive model which simultaneously considers water supply, irrigation system investment, irrigation scheduling, and crop mix selection. A two-stage, mixed integer, nonlinear mathematical programming model under uncertainty, was used to formulate the problem and solved with Benders' decomposition. Pond-based supplemental irrigation is found to be both risk reducing and net income increasing in the study area. The model results also show off-farm water needs to be worth more than $100 per acre foot to make impoundments undesirable.
McCalley, James D.
. The linearized power flow equations are usually used in planning studies of high voltage meshed networks560 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST 2001 A Mixed Integer Disjunctive Model for Transmission Network Expansion Laura Bahiense, Gerson C. Oliveira, Mario Pereira, Member, IEEE
Robert Bixby; Edward Rothberg
2007-01-01
The last few years have been a thrilling time for the commercial application of mixed integer programming. The technology has gone through an inflection point. Just a few years ago, MIP was viewed as a temptingly powerful modeling paradigm that would consistently disappoint in practice. In constrast, in the last few years MIP has become a vital capability that powers
Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach
S. de la Torre; J. M. Arroyo; A. J. Conejo; J. Contreras
2002-01-01
This paper addresses the self-scheduling problem faced by a price-maker to achieve maximum profit in a pool-based electricity market. An exact and computationally efficient mixed-integer linear programming (MILP) formulation of this problem is presented. This formulation models precisely the price-maker capability of altering market-clearing prices to its own benefits, through price quota curves. No assumptions are made on the characteristics
Global Optimization of Mixed-Integer Nonlinear Programs: A Theoretical and Computational
Neumaier, Arnold
awards DMII 95-02722, BES 98-73586, ECS 00-98770, and CTS 01-24751, and the Computational Science. The research was supported in part by ExxonMobil Upstream Research Company, Na- tional Science Foundation, problems in engineering design, logistics, manufacturing, and the chemical and biological sciences often
Grossmann, Ignacio E.
and functions Many new interesting problems Power industrial gases steel SC Oil industrial gases Supply Chain Planning Matrix (Meyr et al., 2002) SugarcaneSugarcane WoodWood Switch grass Switch grass across time scales and functions Many new interesting problems Power industrial gases steel SC Oil
Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software
Jeff Linderoth
2011-11-06
the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.
Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks.
Maurya, M R; Bornheimer, S J; Venkatasubramanian, V; Subramaniam, S
2009-01-01
Quantitative modelling and analysis of biochemical networks is challenging because of the inherent complexities and nonlinearities of the system and the limited availability of parameter values. Even if a mathematical model of the network can be developed, the lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, coarse-grained models (CGMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. The central question in constructing a CGM is which mechanisms should be deemed 'essential' and which can be ignored. Also, how should parameter values be defined when data are sparse? A mixed-integer nonlinear-programming (MINLP) based optimisation approach to coarse-graining is presented. Starting with a detailed biochemical model with associated computational details (reaction network and mathematical description) and data on the biochemical system, the structure and the parameters of a CGM can be determined simultaneously. In this optimisation problem, the authors use a genetic algorithm to simultaneously identify parameter values and remove unimportant reactions. The methodology is exemplified by developing two CGMs for the GTPase-cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G protein signalling 4 [RGS4, a GTPase-activating protein (GAP)] starting from a detailed model of 48 reactions. Both the CGMs have only 17 reactions, fit experimental data well and predict, as does the detailed model, four limiting signalling regimes (LSRs) corresponding to the extremes of receptor and GAP concentration. The authors demonstrate that coarse-graining, in addition to resulting in a reduced-order model, also provides insights into the mechanisms in the network. The best CGM obtained for the GTPase cycle also contains an unconventional mechanism and its predictions explain an old problem in pharmacology, the biphasic (bell-shaped) response to certain drugs. The MINLP methodology is broadly applicable to larger and complex (dense) biochemical modules. PMID:19154082
Mixed-integer programming methods for transportation and power generation problems
NASA Astrophysics Data System (ADS)
Damci Kurt, Pelin
This dissertation conducts theoretical and computational research to solve challenging problems in application areas such as supply chain and power systems. The first part of the dissertation studies a transportation problem with market choice (TPMC) which is a variant of the classical transportation problem in which suppliers with limited capacities have a choice of which demands (markets) to satisfy. We show that TPMC is strongly NP-complete. We consider a version of the problem with a service level constraint on the maximum number of markets that can be rejected and show that if the original problem is polynomial, its cardinality-constrained version is also polynomial. We propose valid inequalities for mixed-integer cover and knapsack sets with variable upper bound constraints, which appear as substructures of TPMC and use them in a branch-and-cut algorithm to solve this problem. The second part of this dissertation studies a unit commitment (UC) problem in which the goal is to minimize the operational cost of power generators over a time period subject to physical constraints while satisfying demand. We provide several exponential classes of multi-period ramping and multi-period variable upper bound inequalities. We prove the strength of these inequalities and describe polynomial-time separation algorithms. Computational results show the effectiveness of the proposed inequalities when used as cuts in a branch-and-cut algorithm to solve the UC problem. The last part of this dissertation investigates the effects of uncertain wind power on the UC problem. A two-stage robust model and a three-stage stochastic program are compared.
Optimal Controlled Variable Selection with Structural Constraints Using MIQP
Skogestad, Sigurd
Optimal Controlled Variable Selection with Structural Constraints Using MIQP Formulations and Technology, NTNU, Trondheim, Norway (e-mail: {skoge}@chemeng.ntnu.no) Abstract: Optimal control structure, structural constraints, Mixed Integer Quadratic Programming. 1. INTRODUCTION Optimal operation of process
Optimization Methods for Minimum Power Bidirectional Topology Construction in Wireless
Arabshahi, Payman
Optimization Methods for Minimum Power Bidirectional Topology Construction in Wireless Networks Abstract-- We consider the problem of minimum power bidi- rectional topology optimization in wireless networks with sectored antennas. We first develop a mixed integer linear programming model for optimal
Zou, Meng; Zhang, Peng-Jun; Wen, Xin-Yu; Chen, Luonan; Tian, Ya-Ping; Wang, Yong
2015-07-15
Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we propose a novel method, MILP_k, to identify serum-based multi-biomarker panel to distinguish colorectal cancers (CRC) from benign colorectal tumors. Specifically, the multi-biomarker panel detection problem is modeled by a mixed integer programming to maximize the classification accuracy. Then we measured the serum profiling data for 101 CRC patients and 95 benign patients. The 61 biomarkers were analyzed individually and further their combinations by our method. We discovered 4 biomarkers as the optimal small multi-biomarker panel, including known CRC biomarkers CEA and IL-10 as well as novel biomarkers IMA and NSE. This multi-biomarker panel obtains leave-one-out cross-validation (LOOCV) accuracy to 0.7857 by nearest centroid classifier. An independent test of this panel by support vector machine (SVM) with threefold cross validation gets an AUC 0.8438. This greatly improves the predictive accuracy by 20% over the single best biomarker. Further extension of this 4-biomarker panel to a larger 13-biomarker panel improves the LOOCV to 0.8673 with independent AUC 0.8437. Comparison with the exhaustive search method shows that our method dramatically reduces the searching time by 1000-fold. Experiments on the early cancer stage samples reveal two panel of biomarkers and show promising accuracy. The proposed method allows us to select the subset of biomarkers with best accuracy to distinguish case and control samples given the number of selected biomarkers. Both receiver operating characteristic curve and precision-recall curve show our method's consistent performance gain in accuracy. Our method also shows its advantage in capturing synergy among selected biomarkers. The multi-biomarker panel far outperforms the simple combination of best single features. Close investigation of the multi-biomarker panel illustrates that our method possesses the ability to remove redundancy and reveals complementary biomarker combinations. In addition, our method is efficient and can select multi-biomarker panel with more than 5 biomarkers, for which the exhaustive methods fail. In conclusion, we propose a promising model to improve the clinical data interpretability and to serve as a useful tool for other complex disease studies. Our small multi-biomarker panel, CEA, IL-10, IMA, and NSE, may provide insights on the disease status of colorectal diseases. The implementation of our method in MATLAB is available via the website: http://doc.aporc.org/wiki/MILP_k. PMID:25980368
Optimization by record dynamics
NASA Astrophysics Data System (ADS)
Barettin, Daniele; Sibani, Paolo
2014-03-01
Large dynamical changes in thermalizing glassy systems are triggered by trajectories crossing record sized barriers, a behavior revealing the presence of a hierarchical structure in configuration space. The observation is here turned into a novel local search optimization algorithm dubbed record dynamics optimization, or RDO. RDO uses the Metropolis rule to accept or reject candidate solutions depending on the value of a parameter akin to the temperature and minimizes the cost function of the problem at hand through cycles where its ‘temperature’ is raised and subsequently decreased in order to expediently generate record high (and low) values of the cost function. Below, RDO is introduced and then tested by searching for the ground state of the Edwards-Anderson spin-glass model, in two and three spatial dimensions. A popular and highly efficient optimization algorithm, parallel tempering (PT), is applied to the same problem as a benchmark. RDO and PT turn out to produce solutions of similar quality for similar numerical effort, but RDO is simpler to program and additionally yields geometrical information on the system’s configuration space which is of interest in many applications. In particular, the effectiveness of RDO strongly indicates the presence of the above mentioned hierarchically organized configuration space, with metastable regions indexed by the cost (or energy) of the transition states connecting them.
Optimization techniques in molecular structure and function elucidation
Sahinidis, Nikolaos V.
2009-01-01
This paper discusses recent optimization approaches to the protein side-chain prediction problem, protein structural alignment, and molecular structure determination from X-ray diffraction measurements. The machinery employed to solve these problems has included algorithms from linear programming, dynamic programming, combinatorial optimization, and mixed-integer nonlinear programming. Many of these problems are purely continuous in nature. Yet, to this date, they have been approached mostly via combinatorial optimization algorithms that are applied to discrete approximations. The main purpose of the paper is to offer an introduction and motivate further systems approaches to these problems. PMID:20160866
Zhang, Xiaodong; Huang, Gordon
2013-02-15
Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p(i) levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences. PMID:23313898
Dasika, M S; Gupta, A; Maranas, C D
2004-01-01
In this paper, an optimization based modeling and solution framework for inferring gene regulatory networks while accounting for time delay is described. The proposed framework uses the basic linear model of gene regulation. Boolean variables are used to capture the existence of discrete time delays between the various regulatory relationships. Subsequently, the time delay that best fits the expression profiles is inferred by minimizing the error between the predicted and experimental expression values. Computational experiments are conducted for both in numero and real expression data sets. The former reveal that if time delay is neglected in a system a priori known to be characterized with time delay then a significantly larger number of parameters are needed to describe the system dynamics. The real microarray data example reveals a considerable number of time delayed interactions suggesting that time delay is ubiquitous in gene regulation. Incorporation of time delay leads to inferred networks that are sparser. Analysis of the amount of variance in the data explained by the model and comparison with randomized data reveals that accounting for time delay explains more variance in real rather than randomized data. PMID:14992526
Integer optimization methods for machine learning
Chang, Allison An
2012-01-01
In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in ...
Analyzing Multi-Objective Linear and Mixed Integer Programs by Lagrange Multipliers
Ramakrishnan, V. S.
A new method for multi-objective optimization of linear and mixed programs based on Lagrange multiplier methods is developed. The method resembles, but is distinct from, objective function weighting and goal programming ...
Optimization for Design and Operation of Natural Gas Transmission Networks
Dilaveroglu, Sebnem 1986-
2012-08-22
and compressor stations. On an existing network, the model also optimizes the total flow through pipelines that satisfy demand to determine the best purchase amount of gas. A mixed integer nonlinear programming model for steady-state natural gas transmission...
Optimized supply routing at Dell under non-stationary demand
Foreman, John William
2008-01-01
This thesis describes the design and implementation of an optimization model to manage inventory at Dell's American factories. Specifically, the model is a mixed integer program which makes routing decisions on incoming ...
2012-01-01
Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON. PMID:22574924
New numerical methods for open-loop and feedback solutions to dynamic optimization problems
NASA Astrophysics Data System (ADS)
Ghosh, Pradipto
The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.
Code Cache Management in Dynamic Optimization Systems
Hazelwood, Kim
Code Cache Management in Dynamic Optimization Systems A thesis presented by Kim Hazelwood Cettei Kim Hazelwood Cettei Code Cache Management in Dynamic Optimization Systems Abstract Dynamic optimization systems store optimized or translated code in software-managed code caches in order to maximize
associated with volatile utility pricing and potentially high system capital costs. Energy technology and boilers), and/or thermal energy storage (e.g., hot water). For some markets, volatile utility pricing heat and power Fuel cells Building energy a b s t r a c t The distributed generation (DG) of combined
DOI 10.1007/s11081-013-9226-6 A mixed-integer nonlinear program for the optimal
in alternative sources of energy for commercial building applications due to their potential to supply on generators or to take advantage of time periods in which utility prices are low. Our research considers
Odd Inge Forsberg; Atle G. Guttormsen
2006-01-01
The most important visual quality characteristic of Atlantic salmon is the red\\/pink flesh color. The primary source of this coloration in salmon is caused by deposition of relatively large amounts of pigments, such as astaxanthin, obtained from their diet. Astaxanthin is expensive, and in commercial farming practice, dietary color pigments comprises about 15–20% of the total feed cost. One important
Grossmann, Ignacio E.
Mellon University, 5000 Forbes Ave, Pittsburgh PA 15232, USA b The Dow Chemical Company, 1776 Building, tank assignment, multi- operation sequencing. Introduction Chemical manufacturing sites ship finished unnecessary shut-downs of the upstream chemical process. When these shutdowns occur, they are said
Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software
Jeff Linderoth
2008-10-10
The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems.
Natural Dynamics for Combinatorial Optimization
Ovchinnikov, Igor V
2015-01-01
Stochastic and or natural dynamical systems (DSs) are dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, earthquakes etc. that exhibit scale-free statistics. These behaviors also occur in many nanosystems. On phase diagrams, these DSs belong to a finite-width phase that separates the phases of thermodynamic equilibrium and ordinary chaotic dynamics, and that is known under such names as intermittency, noise-induced chaos, and self-organized criticality. Within the recently formulated approximation-free cohomological theory of stochastic differential equations, the noise-induced chaos can be roughly interpreted as a noise-induced overlap between regular (integrable) and chaotic (non-integrable) deterministic dynamics so that DSs in this phase inherit the properties of the both. Here, we analyze this unique set of properties and conclude that such DSs must be the most efficient natural optimizers. Based on this understanding, we propose the method of the natural dyn...
Static and Dynamic Optimization Models in Agriculture
NSDL National Science Digital Library
Charles Moss, Associate Professor of Food and Resource Economics at the University of Florida developed this web site for his course on optimization models. The course aims to introduce students to classical optimization models, particularly mathematical programming and using optimal control theory to solve dynamic optimization models. The site provides lecture notes, slides from the lectures, assignments and solutions, and computer programs.
Optimal control with adaptive internal dynamics models
Mitrovic, Djordje; Klanke, Stefan; Vijayakumar, Sethu
2008-01-01
Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics ...
Ameliorating the Overhead of Dynamic Optimization
Zhao, Qin
Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage ...
A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems
Stefan Janson; Martin Middendorf
2004-01-01
\\u000a Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic\\u000a variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical\\u000a PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms\\u000a for a limited number of generations after
Dynamic Pricing through Sampling Based Optimization
Lobel, Ruben
In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm off ers multiple ...
DYNAMIC RISK MANAGEMENT IN ELECTRICITY PORTFOLIO OPTIMIZATION
Römisch, Werner
DYNAMIC RISK MANAGEMENT IN ELECTRICITY PORTFOLIO OPTIMIZATION VIA POLYHEDRAL RISK FUNCTIONALS the dynamic decision structure appropriately. In energy risk management, which is typically carried out ex, for integrating risk management into a stochastic optimization framework, risk has to be quantified in a definite
A Brief GAMS Tutorial for Dynamic Optimization
Grossmann, Ignacio E.
A Brief GAMS Tutorial for Dynamic Optimization L. T. Biegler Chemical Engineering Department Programming Problem s.t. #12;Carnegie Mellon Dynamic Optimization: Methods of Solution Â· GAMS Â requires Â similar to GAMS, more elegant set notation and conditional statements, general formulation with all
Dynamic Optimization for Optimal Control of Water Distribution Systems
Ertin, Emre
Dynamic Optimization for Optimal Control of Water Distribution Systems Emre Ertin, Anthony N. Dean as a controller for a water distribution system. In the example presented we obtain a 12.5 percent reduction for water distribution systems. We consider an isolated district with one water tower and a boosting station
Optimizing cooling and server power consumption
Paolo Cremonesi; Andrea Sansottera; Stefano Gualandi
2011-01-01
This paper proposes new solution strategies for a challenging optimization problem, called Cooling-aware Workload Placement Problem, that looks for a workload placement that optimizes the overall data center power consumption given by the sum of the server power consumption and of the computer room air conditioner power consumption. We formulate CWPP as a Mixed Integer Non Linear Problem using a
Real-time path-planning using mixed-integer linear programming and global cost-to-go maps
Toupet, Olivier
2006-01-01
With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to ...
Optimization of Dynamical Decoupling Using Measurement Feedback
NASA Astrophysics Data System (ADS)
Uys, Hermann; Biercuk, Michael; Vandevender, Aaron; Shiga, Nobuyasu; Itano, Wayne; Bollinger, John
2009-05-01
We study the optimization of dynamical decoupling sequences using ^9Be^+ ions in a Penning ion trap. We artificially synthesize the noise environment the ions see to emulate a variety of physical systems. By incorporating measurement feedback with a Nelder-Mead search algorithm, our locally optimized dynamical decoupling sequences (LODD) attain an order of magnitude improved error suppression compared to known sequences in noise environments with power spectra that have sharp, high-frequency cutoffs. The technique requires no prior knowledge of the noise spectrum. This work shows that optimized dynamical decoupling will be a useful tool in suppressing qubit errors below the fault-tolerant threshold for quantum computation.
Unified Particle Swarm Optimization in Dynamic Environments
Parsopoulos, Konstantinos
Introduction Particle Swarm Optimization (PSO) is a stochastic optimization algorithm that belongs to the category of swarm intelligence methods [1,2]. PSO has attained in- creasing popularity due to its ability a thorough investigation of PSO on a large number of dynamic test problems. Modifications of PSO that can
The Jalapeño dynamic optimizing compiler for Java
Michael G. Burke; Jong-Deok Choi; Stephen J. Fink; David Grove; Michael Hind; Vivek Sarkar; Mauricio J. Serrano; Vugranam C. Sreedhar; Harini Srinivasan; John Whaley
1999-01-01
The Jalapeiio Dynamic Optimizing Compiler is a key com- ponent of the Jalapeiio Virtual Machine, a new Java' Vir- tual Machine (JVM) designed to support efficient and scal- able execution of Java applications on SMP server machines. This paper describes the design of the Jalapefio Optimizing Compiler, and the implementation results that we have ob- tained thus far. To the
Efficient dynamic optimization of logic programs
NASA Technical Reports Server (NTRS)
Laird, Phil
1992-01-01
A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.
Dynamic optimization and adaptive controller design
NASA Astrophysics Data System (ADS)
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
A Robust Approach to Optimal Power Flow With Discrete Variables
Lin Liu; Xifan Wang; Xiaoying Ding; Haoyong Chen
2009-01-01
Optimal power flow (OPF) belongs to the nonlinear optimization problem with discrete variables. The interior point cutting plane method (IPCPM), which possesses the advantages of both the interior point method and the cutting plane method, becomes a very promising approach to the large-scale OPF. It employs a successive linearization process and iteratively solves the mixed integer linear programming problem. However,
Optimized Routing Adaptation in IP Networks Utilizing OSPF and MPLS
Riedl, Anton
Optimized Routing Adaptation in IP Networks Utilizing OSPF and MPLS Anton Riedl Munich University such as OSPF in combination with MPLS. While having the majority of traffic routed along optimized shortest impact. For the setup of complementary MPLS paths, two mixed-integer programming models are proposed
Scalable Offline Optimization of Industrial Wireless Sensor Networks
Luigi Palopoli; Roberto Passerone; Tizar Rizano
2011-01-01
Sensor networks are increasingly used to control and monitor industrial and manufacturing processes. In this paper, we consider the problem of optimizing a cost function for wireless sensor networks of this kind under energy consumption constraints. We focus, in particular, on the problem of coverage optimization through scheduling. Following existing approaches, we use a mixed integer linear program formulation. We
A PSO-based approach to optimal capacitor placement with harmonic distortion consideration
Xin-mei Yu; Xin-yin Xiong; Yao-wu Wu
2004-01-01
This paper presents a particle swarm optimization (PSO) based approach to achieve optimal capacitor placement in radial distribution systems. Harmonic distortion effects, discrete nature of capacitors, and different load levels are all taken into consideration in the problem formulation. Mathematically, the capacitor placement problem is a non-linear and non-differentiable mixed integer optimization problem with a set of equality and inequality
Role of controllability in optimizing quantum dynamics
Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel [Department of Automation, Tsinghua University, Beijing, 100084, China and Center for Quantum Information Science and Technology, TNList, Beijing, 100084 (China); Department of Chemistry and Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90025 (United States); Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)
2011-06-15
This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.
A Clustering Particle Swarm Optimizer for Dynamic Optimization Changhe Li and Shengxiang Yang
Yang, Shengxiang
A Clustering Particle Swarm Optimizer for Dynamic Optimization Changhe Li and Shengxiang Yang that optimiza- tion algorithms need to not only find the global optimal solution but also track the trajectory particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical
Optimal BLS: Optimizing transit-signal detection for Keplerian dynamics
NASA Astrophysics Data System (ADS)
Ofir, Aviv
2015-08-01
Transit surveys, both ground- and space-based, have already accumulated a large number of light curves that span several years. We optimize the search for transit signals for both detection and computational efficiencies by assuming that the searched systems can be described by Keplerian, and propagating the effects of different system parameters to the detection parameters. Importnantly, we mainly consider the information content of the transit signal and not any specific algorithm - and use BLS (Kovács, Zucker, & Mazeh 2002) just as a specific example.We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually.By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the transit signal parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets, while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available.
STABILITY OF SIMULTANEOUS RECURRENT NEURAL NETWORK DYNAMICS FOR STATIC OPTIMIZATION
Serpen, Gursel
STABILITY OF SIMULTANEOUS RECURRENT NEURAL NETWORK DYNAMICS FOR STATIC OPTIMIZATION Gursel Serpen stability analysis of the dynamics of a relaxation-based recurrent neural network, the Simultaneous of static optimization problems in a computationally efficient manner, is studied. Specifically, local
IMPACT OF DYNAMIC VOLTAGE SCALING (DVS) ON CIRCUIT OPTIMIZATION
Esquit Hernandez, Carlos A.
2010-01-16
Circuit designers perform optimization procedures targeting speed and power during the design of a circuit. Gate sizing can be applied to optimize for speed, while Dual-VT and Dynamic Voltage Scaling (DVS) can be applied to optimize for leakage...
Constructing Dynamic Optimization Test Problems Using the Multi-objective
Jin, Yaochu
-objective optimization (MOO) concepts. By aggregating different objectives of an MOO problem and changing the weights-objective optimization and thus the rich MOO test problems can easily be adapted to dynamic optimization test functions
Optimizing Motion Planning for Hyper Dynamic Manipulator
NASA Astrophysics Data System (ADS)
Aboura, Souhila; Omari, Abdelhafid; Meguenni, Kadda Zemalache
2012-01-01
This paper investigates the optimal motion planning for an hyper dynamic manipulator. As case study, we consider a golf swing robot which is consisting with two actuated joint and a mechanical stoppers. Genetic Algorithm (GA) technique is proposed to solve the optimal golf swing motion which is generated by Fourier series approximation. The objective function for GA approach is to minimizing the intermediate and final state, minimizing the robot's energy consummation and maximizing the robot's speed. Obtained simulation results show the effectiveness of the proposed scheme.
Application of optimal prediction to molecular dynamics
Barber IV, John Letherman
2004-12-01
Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is {delta}-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.
Dynamics of Dengue epidemics using optimal control
Rodrigues, Helena Sofia; Torres, Delfim F M
2010-01-01
We present an application of optimal control theory to Dengue epidemics. This epidemiologic disease is an important theme in tropical countries due to the growing number of infected individuals. The dynamic model is described by a set of nonlinear ordinary differential equations, that depend on the dynamic of the Dengue mosquito, the number of infected individuals, and the people's motivation to combat the mosquito. The cost functional depends not only on the costs of medical treatment of the infected people but also on the costs related to educational and sanitary campaigns. Two approaches to solve the problem are considered: one using optimal control theory, another one by discretizing first the problem and then solving it with nonlinear programming. The results obtained with OC-ODE and IPOPT solvers are given and discussed. We observe that with current computational tools it is easy to obtain, in an efficient way, better solutions to Dengue problems, leading to a decrease of infected mosquitoes and individ...
Direct Optimal Control of Duffing Dynamics
NASA Technical Reports Server (NTRS)
Oz, Hayrani; Ramsey, John K.
2002-01-01
The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.
Rigorous bounds for optimal dynamical decoupling
Uhrig, Goetz S. [Lehrstuhl fuer Theoretische Physik I, Technische Universitaet Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany); Lidar, Daniel A. [Departments of Chemistry, Electrical Engineering, and Physics, Center for Quantum Information and Technology, University of Southern California, Los Angeles, California 90089 (United States)
2010-07-15
We present rigorous performance bounds for the optimal dynamical decoupling pulse sequence protecting a quantum bit (qubit) against pure dephasing. Our bounds apply under the assumption of instantaneous pulses and of bounded perturbing environment and qubit-environment Hamiltonians such as those realized by baths of nuclear spins in quantum dots. We show that if the total sequence time is fixed the optimal sequence can be used to make the distance between the protected and unperturbed qubit states arbitrarily small in the number of applied pulses. If, on the other hand, the minimum pulse interval is fixed and the total sequence time is allowed to scale with the number of pulses, then longer sequences need not always be advantageous. The rigorous bound may serve as a testbed for approximate treatments of optimal decoupling in bounded models of decoherence.
Baliban, Richard C.; DiMaggio, Peter A.; Plazas-Mayorca, Mariana D.; Garcia, Benjamin A.; Floudas, Christodoulos A.
2012-01-01
A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu. PMID:22788846
Integrated DFM Framework for Dynamic Yield Optimization
NSDL National Science Digital Library
This website includes an abstract of the following article. Users may request access to the full article via the website, and a direct link will be emailed to them. We present a new methodology for a balanced yield optimization and a new DFM (design for manufacturability) framework which implements it. Our approach allows designers to dynamically balance multiple factors contributing to yield loss and select optimal combination of DFM enhancements based on the current information about the IC layout, the manufacturing process, and known causes of failures. We bring together the information gained from layout analysis, layout aware circuit analysis, resolution enhancement and optical proximity correction tools, parasitics extraction, timing estimates, and other tools, to suggest the DFM solution which is optimized within the existing constraints on design time and available data. The framework allows us to integrate all available sources of yield information, characterize and compare proposed DFM solutions, quickly adjust them when new data or new analysis tools become available, fine tune DFM optimization for a particular design and process and provide the IC designer with a customized solution which characterizes the manufacturability of the design, identifies and classifies areas with the most opportunities for improvement, and suggests DFM improvements. The proposed methodology replaces the ad hoc approach to DFM which targets one yield loss cause at a time at the expense of other factors with a comprehensive analysis of competing DFM techniques and trade offs between them.
Optimal Control of Switched Dynamical Systems under Dwell Time Constraints
Egerstedt, Magnus
Optimal Control of Switched Dynamical Systems under Dwell Time Constraints Usman Ali1 and Magnus Egerstedt2 Abstract-- This paper addresses the problem of optimally scheduling the mode sequence and mode functional defined on the state trajectory. The topology of the optimization space for switched dynamical
Mobile Robotic Systems: Dynamics, Control, and Optimization
NASA Astrophysics Data System (ADS)
Chernousko, F. L.
2009-08-01
Several classes of mobile robotic systems are discussed that are based on certain non-conventional principles of motion and can move along different surfaces and inside various media. Namely, we consider wall-climbing robots equipped with pneumatic grippers and able to move along vertical walls; snake-like multilink mechanisms with actuators installed at their joints; and vibro-robots moving in resistive media and containing movable masses. Kinematics and dynamics of these types of robots are discussed. Optimal geometrical and mechanical parameters as well as optimal periodic motions of robots are determined that correspond to the maximal average speed of locomotion. Results of experiments with prototypes of robots as well as results of computer simulation are presented. The locomotion principles analyzed are applicable to robots that can move in a complicated and hazardous environment, along different surfaces, and inside tubes.
Robust optimization with transiently chaotic dynamical systems
NASA Astrophysics Data System (ADS)
Sumi, R.; Molnár, B.; Ercsey-Ravasz, M.
2014-05-01
Efficiently solving hard optimization problems has been a strong motivation for progress in analog computing. In a recent study we presented a continuous-time dynamical system for solving the NP-complete Boolean satisfiability (SAT) problem, with a one-to-one correspondence between its stable attractors and the SAT solutions. While physical implementations could offer great efficiency, the transiently chaotic dynamics raises the question of operability in the presence of noise, unavoidable on analog devices. Here we show that the probability of finding solutions is robust to noise intensities well above those present on real hardware. We also developed a cellular neural network model realizable with analog circuits, which tolerates even larger noise intensities. These methods represent an opportunity for robust and efficient physical implementations.
Superstructure Optimization for Oil Refinery Design
C. S. Khor; A. Elkamel
2010-01-01
In this work, the optimal petroleum refinery topology is formulated as a conceptual process synthesis problem using the superstructure optimization approach. We begin with the development of a state–task network (STN)-based superstructure representation that is sufficiently rich to encompass all possible topology alternatives of a conventional oil refinery. Subsequently, a biobjective mixed-integer linear program (MILP) of maximization of profit and
Optimal Empirical Prognostic Models of Climate Dynamics
NASA Astrophysics Data System (ADS)
Loskutov, E. M.; Mukhin, D.; Gavrilov, A.; Feigin, A. M.
2014-12-01
In this report the empirical methodology for prediction of climate dynamics is suggested. We construct the dynamical models of data patterns connected with climate indices, from observed spatially distributed time series. The models are based on artificial neural network (ANN) parameterization and have a form of discrete stochastic evolution operator mapping some sequence of systems state on the next one [1]. Different approaches to reconstruction of empirical basis (phase variables) for system's phase space representation, which is appropriate for forecasting the climate index of interest, are discussed in the report; for this purpose both linear and non-linear data expansions are considered. The most important point of the methodology is finding the optimal structural parameters of the model such as dimension of variable vector, i.e. number of principal components used for modeling, the time lag used for prediction, and number of neurons in ANN determining the quality of approximation. Actually, we need to solve the model selection problem, i.e. we want to obtain a model of optimal complexity in relation to analyzed time series. We use MDL approach [2] for this purpose: the model providing best data compression is chosen. The method is applied to space-distributed time-series of sea surface temperature and sea level pressure taken from IRI datasets [3]: the ability of proposed models to predict different climate indices (incl. Multivariate ENSO index, Pacific Decadal Oscillation index, North-Atlantic Oscillation index) is investigated. References:1. Molkov Ya. I., E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series. Phys. Rev. E, 85, 036216, 2012.2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009.3. IRI/LDEO Climate Data Library (http://iridl.ldeo.columbia.edu/)
Structural optimization for nonlinear dynamic response.
Dou, Suguang; Strachan, B Scott; Shaw, Steven W; Jensen, Jakob S
2015-09-28
Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped-clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order of magnitude by relatively simple changes in the shape of these elements. We expect the proposed approach, and its extensions, to be useful for the design of systems used for fundamental studies of nonlinear behaviour as well as for the development of commercial devices that exploit nonlinear behaviour. PMID:26303922
Nonsmooth dynamic optimization of systems with varying structure
Yunt, Mehmet, 1975-
2011-01-01
In this thesis, an open-loop numerical dynamic optimization method for a class of dynamic systems is developed. The structure of the governing equations of the systems under consideration change depending on the values of ...
Dynamic optimization of a copolymerization reactor using tabu search.
Anand, P; Rao, M Bhagvanth; Venkateswarlu, Ch
2015-03-01
A novel multistage dynamic optimization strategy based on meta-heuristic tabu search (TS) is proposed and evaluated through sequential and simultaneous implementation procedures by applying it to a semi-batch styrene-acrylonitrile (SAN) copolymerization reactor. The adaptive memory and responsive exploration features of TS are exploited to design the dynamic optimization strategy and compute the optimal control policies for temperature and monomer addition rate so as to achieve the desired product quality parameters expressed in terms of single and multiple objectives. The dynamic optimization results of TS sequential and TS simultaneous implementation strategies are analyzed and compared with those of a conventional optimization technique based on iterative dynamic programming (IDP). The simulation results demonstrate the usefulness of TS for optimal control of transient dynamic systems. PMID:25466914
Algorithms for Pseudo-Boolean Optimization Using Gomory Cuts and Search Restarts
Vasco M. Manquinho
Cutting planes are a well-known, widely used, and very effective technique for Integer Linear Programming (ILP). In contrast, the utilization of cutting planes in Pseudo- Boolean Optimization (PBO) is recent and results still pre- liminary. This paper addresses the utilization of cutting planes, namely Gomory mixed-integer cuts, in Satisability- based algorithms for PBO, and shows how these cuts can be
Optimal flow rates and well locations for soil vapor extraction design
Charles S. Sawyer; Madhavi Kamakoti
1998-01-01
A mixed-integer programming model to determine the optimum number of wells, their locations and pumping rates for soil vapor extraction (SVE) is developed by coupling an air flow simulation model (AIR3D) to the GAMS optimization software. The model was tested for sensitivity of the vertical discretization of the domain, the number of potential well locations, the number of constraints, and
CloudOpt: Multi-Goal Optimization of Application Deployments across a Cloud , Murray Woodside1
Woodside, C. Murray
the applications in the cloud, each with many processes. Quality of service (QoS) contracts must be satisfiedOpt is a comprehensive approach to find optimal deployments for large service centers and clouds. It uses a combination of bin-packing, mixed integer programming and performance models to make decisions affecting diverse
Optimality-based Bound Contraction with Multiparametric Disaggregation for the Global
Grossmann, Ignacio E.
unit commitment problem [19], which is a quadratic function of power; (ii) the power output in hydro power system, we show that this can be an efficient approach depending on the problem size. The relaxed. The global optimization of mixed-integer nonlinear problem (P) is important in areas such as power systems
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
Optimal dynamic discrimination of similar quantum systems
NASA Astrophysics Data System (ADS)
Li, Baiqing
2005-07-01
The techniques for identifying and separating similar molecules have always been very important to chemistry and other branches of science and engineering. Similar quantum systems share comparable Hamiltonians, so their eigenenergy levels, transition dipole moments, and therefore their ordinary observable properties are alike. Traditional analytical methods have mostly been restricted by working with the subtle differences in the physical and chemical properties of the similar species. Optimal Dynamic Discrimination (ODD) aims at magnifying the dissimilarity of the agents by actively controlling their quantum evolution, drawing on the extremely rich information embedded in their dynamics. ODD is developed based on the tremendous flexibility of Optimal Control Theory (OCT) and on the practical implementation of closed-loop learning control, which has become a more and more indispensable tool for controlling quantum processes. The ODD experimental paradigm is designed to combat a number of factors that are detrimental to the discrimination of similar molecules: laser pulse noise, signal detection errors, finite time resolution in the signals, and environmental decoherence effects. It utilizes either static signals or time series signal, the latter capable of providing more information. Simulations are performed in this dissertation progressing from the wave function to the density matrix formulation, in order to study the decoherence effects. Analysis of the results reveals the roles of the adverse factors, unravels the underlying mechanisms of ODD, and provides insights on laboratory implementation. ODD emphasizes the incorporation of algorithmic development and laboratory design, and seeks to bridge the gap between theoretical/computational chemistry and experimental chemistry, with the help from applied mathematics and computer science.
Online optimization of storage ring nonlinear beam dynamics
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2015-08-01
We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.
Decentralized optimal control of dynamical systems under uncertainty
NASA Astrophysics Data System (ADS)
Gabasov, R.; Dmitruk, N. M.; Kirillova, F. M.
2011-07-01
The problem of optimal control of a group of interconnected dynamical objects under uncertainty is considered. The cases are examined in which the centralized control of the group of objects is impossible due to delay in the channel for information exchange between the group members. Optimal self-control algorithms in real time for each dynamical object are proposed. Various types of a priori and current information about the behavior of the group members and about uncertainties in the system are examined. The proposed methods supplement the earlier developed optimal control methods for an individual dynamical system and the methods of decentralized optimal control of deterministic objects. The results are illustrated with examples.
Online optimization of storage ring nonlinear beam dynamics
Huang, Xiaobiao
2015-01-01
We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.
Dynamic multiobective optimization of power plant using PSO techniques
J. S. Heo; K. Y. Lee; R. Garduno-Ramirez
2005-01-01
The Coordinate Control Scheme (CCS) requires references to provide control inputs to a power plant. The references are obtained by mapping the unit load demand to pressure set-point. In order to achieve the optimal power plant operation, the mapping should be optimized under a dynamic environment by considering the multiobjectives of the power system. In this paper, the multiobjective optimal
Optimal control of HIV/AIDS dynamic: Education and treatment
NASA Astrophysics Data System (ADS)
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
Optimal Verification of Operations on Dynamic Sets Charalampos Papamanthou
International Association for Cryptologic Research (IACR)
Optimal Verification of Operations on Dynamic Sets Charalampos Papamanthou Brown University accumulation tree, our authenticated data structure is the first to achieve optimal verification and proof complexity (i.e., only proportional to the size of the query parameters and the answer), as well as optimal
Proportional Integral Distributed Optimization for Dynamic Network Topologies
Egerstedt, Magnus
Proportional Integral Distributed Optimization for Dynamic Network Topologies Greg Droge, Magnus topologies. Methods for distributed optimization have been cate- gorized into two groups based optimization for switch- ing topologies, a brief background is given in Section II. Then, Section III and IV
Chaotic dynamics in optimal monetary policy
NASA Astrophysics Data System (ADS)
Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.
2007-05-01
There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.
Dual Population-Based Incremental Learning for Problem Optimization in Dynamic Environments
Yang, Shengxiang
Dual Population-Based Incremental Learning for Problem Optimization in Dynamic Environments: dynamic optimization, population-based incremental learning, dualism, evolutionary algorithms 1 of Population-Based Incremental Learning (PBIL) algorithms, a class of EAs, for solving dynamic optimization
Dynamic systems of regional economy management optimization
NASA Astrophysics Data System (ADS)
Trofimov, S.; Kudzh, S.
One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major directions of an industrial policy of region. The situational-analytical centers (SAC) of regional administration The major component of SAC is dynamic modeling, analysis, forecasting and optimization systems, based on modern intellectual information technologies. Spheres of SAC are not only financial streams management and investments optimization, but also strategic forecasting functions, which provide an optimum choice, "aiming", search of optimum ways of regional development and corresponding investments. It is expedient to consider an opportunity of formation of the uniform organizational-methodical center of an industrial policy of region. This organization can be directly connected to the scheduled-analytical services of the largest economic structures, local authorities, the ministries and departments. Such "direct communication" is capable to provide an effective regional development strategic management. Anyway, the output on foreign markets demands concentration of resources and support of authorities. Offered measures are capable to provide a necessary coordination of efforts of a various level economic structures. For maintenance of a regional industrial policy an attraction of all newest methods of strategic planning and management is necessary. Their activity should be constructed on the basis of modern approaches of economic systems management, cause the essence of an industrial policy is finally reduced to an effective regional and corporate economic activities control centers formation. Opportunities of optimum regional economy planning and management as uniform system Approaches to planning regional economic systems can be different. We will consider some most effective methods of planning and control over a regional facilities condition. All of them are compact and evident, that allows to put them into the group of average complexity technologies. At the decision of problems of a regional resource management is rather perspective the so-called "topographical" approach, which is used by intellectual information tec
Structural optimization with constraints from dynamics in Lagrange
NASA Technical Reports Server (NTRS)
Pfeiffer, F.; Kneppe, G.; Ross, C.
1990-01-01
Structural optimization problems are mostly solved under constraints from statics, such as stresses, strains, or displacements under static loads. But in the design process, dynamic quantities like eigenfrequencies or accelerations under dynamic loads become more and more important. Therefore, it is obvious that constraints from dynamics must be considered in structural optimization packages. This paper addresses the dynamics branch in MBB-LAGRANGE. It will concentrate on two topics, namely on the different formulations for eigenfrequency constraints and on frequency response constraints. For the latter the necessity of a system reduction is emphasized. The methods implemented in LAGRANGE are presented and examples are given.
Optimal dynamic pricing for clearance sales on the spot market
Ileri, F?rat
2009-01-01
In this thesis, we develop an optimal dynamic pricing strategy for clearance sales of a textile manufacturing company which allows last-minute cancellations of orders without penalty. This company faces the difficult task ...
Validated global multiobjective optimization of trajectories in nonlinear dynamical systems
Coffee, Thomas Merritt
2015-01-01
We introduce a new approach for global multiobjective optimization of trajectories in continuous nonlinear dynamical systems that can provide rigorous, arbitrarily tight bounds on the objective values and state paths ...
Method to describe stochastic dynamics using an optimal coordinate.
Krivov, Sergei V
2013-12-01
A general method to describe the stochastic dynamics of Markov processes is suggested. The method aims to solve three related problems: the determination of an optimal coordinate for the description of stochastic dynamics; the reconstruction of time from an ensemble of stochastic trajectories; and the decomposition of stationary stochastic dynamics into eigenmodes which do not decay exponentially with time. The problems are solved by introducing additive eigenvectors which are transformed by a stochastic matrix in a simple way - every component is translated by a constant distance. Such solutions have peculiar properties. For example, an optimal coordinate for stochastic dynamics with detailed balance is a multivalued function. An optimal coordinate for a random walk on a line corresponds to the conventional eigenvector of the one-dimensional Dirac equation. The equation for the optimal coordinate in a slowly varying potential reduces to the Hamilton-Jacobi equation for the action function. PMID:24483410
An Optimization Framework for Dynamic Hybrid Energy Systems
Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis
2014-03-01
A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.
Use of Regularization Functions in Problems of Dynamic Optimization
Grossmann, Ignacio E.
Functions in Problems of Dynamic Optimization 7 Numerical Applications 1. Condenser-Tank - Pantelides(1988 in Problems of Dynamic Optimization 8 Switching between models allowing continuous integration of the problem , with 0 0 , , with 0 1 where : 1 with and 0 final u t s p s p te f P t S S P P K S K dx S P x x g Sdt S P
An Optimization Framework for Dynamic, Distributed Real-Time Systems
NASA Technical Reports Server (NTRS)
Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara
2003-01-01
Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.
Transmission dynamics and optimal control of measles epidemics q
Ruan, Shigui
Transmission dynamics and optimal control of measles epidemics q Liuyong Pang a,c , Shigui Ruan b o Keywords: Measles Endemic Epidemic cycles Vaccination strategy Optimal control a b s t r a c t Based on the mechanism and characteristics of measles transmission, we propose a susceptible
DYNAMIC EMBEDDED OPTIMIZATION AND SHOOTING METHODS FOR POWER
for Deregulated Electric Power Systems: Optimization, Control, and Computational Intelligence, J. Chow, F. Wu arising from system dynamic behavior can also be thought of in an optimization framework. However consequences in the latter case [1]. Even the tuning of traditional controllers such as power system sta
First principles molecular dynamics without self-consistent field optimization
Souvatzis, Petros; Niklasson, Anders M. N.
2014-01-28
We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations.
Compound Particle Swarm Optimization in Dynamic Environments
Yang, Shengxiang
applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO behavior of particle swarms from the domain of physics is integrated into PSO and a compound particle swarm is a population based optimization technique with the inspiration from the social behavior of a swarm of birds
Optimized dynamical control of state transfer through noisy spin chains
Analia Zwick; Gonzalo A. Alvarez; Guy Bensky; Gershon Kurizki
2015-01-09
We propose a method of optimally controlling the tradeoff of speed and fidelity of state transfer through a noisy quantum channel (spin-chain). This process is treated as qubit state-transfer through a fermionic bath. We show that dynamical modulation of the boundary-qubits levels can ensure state transfer with the best tradeoff of speed and fidelity. This is achievable by dynamically optimizing the transmission spectrum of the channel. The resulting optimal control is robust against both static and fluctuating noise in the channel's spin-spin couplings. It may also facilitate transfer in the presence of diagonal disorder (on site energy noise) in the channel.
Neural dynamic optimization for control systems. I. Background.
Seong, C Y; Widrow, B
2001-01-01
The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively. PMID:18244815
Neural dynamic optimization for control systems.II. Theory.
Seong, C Y; Widrow, B
2001-01-01
The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively. PMID:18244816
Dynamic positioning configuration and its first-order optimization
NASA Astrophysics Data System (ADS)
Xue, Shuqiang; Yang, Yuanxi; Dang, Yamin; Chen, Wu
2014-02-01
Traditional geodetic network optimization deals with static and discrete control points. The modern space geodetic network is, on the other hand, composed of moving control points in space (satellites) and on the Earth (ground stations). The network configuration composed of these facilities is essentially dynamic and continuous. Moreover, besides the position parameter which needs to be estimated, other geophysical information or signals can also be extracted from the continuous observations. The dynamic (continuous) configuration of the space network determines whether a particular frequency of signals can be identified by this system. In this paper, we employ the functional analysis and graph theory to study the dynamic configuration of space geodetic networks, and mainly focus on the optimal estimation of the position and clock-offset parameters. The principle of the D-optimization is introduced in the Hilbert space after the concept of the traditional discrete configuration is generalized from the finite space to the infinite space. It shows that the D-optimization developed in the discrete optimization is still valid in the dynamic configuration optimization, and this is attributed to the natural generalization of least squares from the Euclidean space to the Hilbert space. Then, we introduce the principle of D-optimality invariance under the combination operation and rotation operation, and propose some D-optimal simplex dynamic configurations: (1) (Semi) circular configuration in 2-dimensional space; (2) the D-optimal cone configuration and D-optimal helical configuration which is close to the GPS constellation in 3-dimensional space. The initial design of GPS constellation can be approximately treated as a combination of 24 D-optimal helixes by properly adjusting the ascending node of different satellites to realize a so-called Walker constellation. In the case of estimating the receiver clock-offset parameter, we show that the circular configuration, the symmetrical cone configuration and helical curve configuration are still D-optimal. It shows that the given total observation time determines the optimal frequency (repeatability) of moving known points and vice versa, and one way to improve the repeatability is to increase the rotational speed. Under the Newton's law of motion, the frequency of satellite motion determines the orbital altitude. Furthermore, we study three kinds of complex dynamic configurations, one of which is the combination of D-optimal cone configurations and a so-called Walker constellation composed of D-optimal helical configuration, the other is the nested cone configuration composed of n cones, and the last is the nested helical configuration composed of n orbital planes. It shows that an effective way to achieve high coverage is to employ the configuration composed of a certain number of moving known points instead of the simplex configuration (such as D-optimal helical configuration), and one can use the D-optimal simplex solutions or D-optimal complex configurations in any combination to achieve powerful configurations with flexile coverage and flexile repeatability. Alternately, how to optimally generate and assess the discrete configurations sampled from the continuous one is discussed. The proposed configuration optimization framework has taken the well-known regular polygons (such as equilateral triangle and quadrangular) in two-dimensional space and regular polyhedrons (regular tetrahedron, cube, regular octahedron, regular icosahedron, or regular dodecahedron) into account. It shows that the conclusions made by the proposed technique are more general and no longer limited by different sampling schemes. By the conditional equation of D-optimal nested helical configuration, the relevance issues of GNSS constellation optimization are solved and some examples are performed by GPS constellation to verify the validation of the newly proposed optimization technique. The proposed technique is potentially helpful in maintenance and quadratic optimization of single GNSS of which the orbi
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems
Yang, Shengxiang
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems Shengxiang Yang.yang@mcs.le.ac.uk ABSTRACT This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance
Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization
ERIC Educational Resources Information Center
Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.
2013-01-01
Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic…
Optimizing MATLAB feval with Dynamic Techniques Nurudeen Lameed Laurie Hendren
Optimizing MATLAB feval with Dynamic Techniques Nurudeen Lameed Laurie Hendren Sable Research Group,hendren}@cs.mcgill.ca, http://www.sable.mcgill.ca/mclab Abstract MATLAB is a popular dynamic array-based language used by en- gineers, scientists and students worldwide. The built-in function feval is an important MATLAB feature
Structural optimization of rotor blades with integrated dynamics and aerodynamics
NASA Technical Reports Server (NTRS)
Chattopadhyay, Aditi; Walsh, Joanne L.
1989-01-01
The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.
Structural optimization of rotor blades with integrated dynamics and aerodynamics
NASA Technical Reports Server (NTRS)
Chattopadhyay, Aditi; Walsh, Joanne L.
1988-01-01
The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.
Practical synchronization on complex dynamical networks via optimal pinning control.
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications. PMID:26274112
Practical synchronization on complex dynamical networks via optimal pinning control
NASA Astrophysics Data System (ADS)
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Optimization in the Now: Dynamic Peephole Optimization for Hierarchical Planning
Lozano-Perez, Tomas
plan and often arrives at poor quality plans. This paper outlines a method for dynamically improving of an abstract operator and propose a general way to approach estimating them. We ran experiments in challenging operations of forklifts for loading, unloading, and arranging packages within a truck or airplane, as well
Fault tolerant and dynamic evolutionary optimization engines
Morales Reyes, Alicia
2011-01-01
Mimicking natural evolution to solve hard optimization problems has played an important role in the artificial intelligence arena. Such techniques are broadly classified as Evolutionary Algorithms (EAs) and have been ...
Stochastic optimal control with learned dynamics models
Mitrovic, Djordje
2011-01-01
The motor control of anthropomorphic robotic systems is a challenging computational task mainly because of the high levels of redundancies such systems exhibit. Optimality principles provide a general strategy to resolve ...
Combining Optimal Control Theory and Molecular Dynamics for Protein Folding
Arkun, Yaman; Gur, Mert
2012-01-01
A new method to develop low-energy folding routes for proteins is presented. The novel aspect of the proposed approach is the synergistic use of optimal control theory with Molecular Dynamics (MD). In the first step of the method, optimal control theory is employed to compute the force field and the optimal folding trajectory for the atoms of a Coarse-Grained (CG) protein model. The solution of this CG optimization provides an harmonic approximation of the true potential energy surface around the native state. In the next step CG optimization guides the MD simulation by specifying the optimal target positions for the atoms. In turn, MD simulation provides an all-atom conformation whose positions match closely the reference target positions determined by CG optimization. This is accomplished by Targeted Molecular Dynamics (TMD) which uses a bias potential or harmonic restraint in addition to the usual MD potential. Folding is a dynamical process and as such residues make different contacts during the course of folding. Therefore CG optimization has to be reinitialized and repeated over time to accomodate these important changes. At each sampled folding time, the active contacts among the residues are recalculated based on the all-atom conformation obtained from MD. Using the new set of contacts, the CG potential is updated and the CG optimal trajectory for the atoms is recomputed. This is followed by MD. Implementation of this repetitive CG optimization - MD simulation cycle generates the folding trajectory. Simulations on a model protein Villin demonstrate the utility of the method. Since the method is founded on the general tools of optimal control theory and MD without any restrictions, it is widely applicable to other systems. It can be easily implemented with available MD software packages. PMID:22238629
On Using Cutting Planes in Pseudo-Boolean Optimization
Vasco M. Manquinho; João P. Marques Silva
2006-01-01
Cutting planes are a well-known, widely used, and very eectiv e technique for Integer Linear Programming (ILP). However, cutting plane techniques are seldom used in Pseudo- Boolean Optimization (PBO) algorithms. This paper addresses the utilization of Gomory mixed-integer and clique cuts, in Satisabilit y-based algorithms for PBO, and shows how these cuts can be used for computing lower bounds and
OPTIMAL TECHNOLOGY SELECTION AND OPERATION OF MICROGRIDS IN COMMERCIAL BUILDINGS
Chris MARNAY; Giri VENKATARAMANAN; Michael STADLER; Afzal SIDDIQUI; Ryan FIRESTONE; Bala CHANDRAN
DER-CAM is a mixed-integer linear program implemented in GAMS that solves the commercial building DER investment optimization problem given a building's end-use energy loads, energy tariff structures and fuel prices, and an arbitrary list of equipment investment options. The approach is fully technology-neutral and can include energy purchases, on-site conversion, both electrical and thermal onsite harvesting, and end-use efficiency investments,
Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.
1992-01-01
This paper describes a fully integrated aerodynamic/dynamic optimization procedure for helicopter rotor blades. The procedure combines performance and dynamics analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuver; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case the objective function involves power required (in hover, forward flight, and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.
Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.
1992-01-01
A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.
Aerospace applications of integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in solving combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on a large space structure and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Aerospace applications on integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem. for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Aerospace Applications of Integer and Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II
Yang, L.; Li, Y.; Guo, W.; Krinsky, S.
2011-03-28
In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.
Optimal stabilization policy in a model of elasticity dynamics
Bhandari, J.S.; Hanson, D.A.
1983-01-01
This paper considers the role of optimal fiscal policy in the context of a macrodynamic model of the open economy. The dynamics of the model are governed by the transition of aggregate demand elasticities from their short-run values to long-run values. If there are no instrument costs attributed to government expenditure, then optimal fiscal policy can achieve perfect stabilization of price and exchange-rate levels. With instrument costs however, this is no longer possible and the optimal fiscal rule is of the linear feedback variety. The paper investigates the properties of this rule.
Optimal entangling capacity of dynamical processes
Campbell, Earl T. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom)
2010-10-15
We investigate the entangling capacity of dynamical operations when provided with local ancilla. A comparison is made between the entangling capacity with and without the assistance of prior entanglement. An analytic solution is found for the log-negativity entangling capacity of two-qubit gates, which equals the entanglement of the Choi matrix isomorphic to the unitary operator. Surprisingly, the availability of prior entanglement does not affect this result, a property we call resource independence of the entangling capacity. We prove several useful upper bounds on the entangling capacity that hold for general qudit dynamical operations and for a whole family of entanglement monotones including log negativity and log robustness. The log-robustness entangling capacity is shown to be resource independent for general dynamics. We provide numerical results supporting a conjecture that the log-negativity entangling capacity is resource independent for all two-qudit unitary operators.
Speeding up critical system dynamics through optimized evolution
Caneva, Tommaso [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Calarco, Tommaso; Montangero, Simone [Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Fazio, Rosario [NEST, Scuola Normale Superiore and Istituto di Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126 Pisa (Italy); Santoro, Giuseppe E. [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); CNR-INFM Democritos National Simulation Center, Via Beirut 2-4, I-34014 Trieste (Italy); International Centre for Theoretical Physics (ICTP), P.O. Box 586, I-34014 Trieste (Italy)
2011-07-15
The number of defects which are generated upon crossing a quantum phase transition can be minimized by choosing properly designed time-dependent pulses. In this work we determine what are the ultimate limits of this optimization. We discuss under which conditions the production of defects across the phase transition is vanishing small. Furthermore we show that the minimum time required to enter this regime is T{approx}{pi}/{Delta}, where {Delta} is the minimum spectral gap, unveiling an intimate connection between an optimized unitary dynamics and the intrinsic measure of the Hilbert space for pure states. Surprisingly, the dynamics is nonadiabatic; this result can be understood by assuming a simple two-level dynamics for the many-body system. Finally we classify the possible dynamical regimes in terms of the action s=T{Delta}.
Practicing JUDO: Java under dynamic optimizations
Michal Cierniak; Guei-Yuan Lueh; James M. Stichnoth
2000-01-01
ABSTRACT A high - performance implementation of a Java Virtual Machine (JVM) consists of efficient implementation of Just - In - Time (JIT) compilation, exception handling, synchronization mechanism, and garbage collection (GC) These components are tightly coupled to achieve high performance In this paper, we present some static and dynamic techniques implemented in the JIT compilation and exception handling of
Optimal Gradient Clock Synchronization in Dynamic Networks
Zurich Switzerland thl@zurich.ibm.com Rotem Oshman MIT Computer Science and Artificial Intelligence Lab apply to our highly dynamic setting: if two nodes remain at distance d from each other for sufficiently hardware clock, which can be used for this pur- pose; however, hardware clocks of different nodes run
Dynamic optimization of district energy grid
NASA Astrophysics Data System (ADS)
Salsbery, Scott
The University of Iowa Power Plant operates utility generation and distribution for campus facilities, including electricity, steam, and chilled water. It is desirable to evaluate the optimal load combination of boilers, engines and chillers to meet the demand at minimal cost, particularly for future demand scenarios. An algorithm has been developed which takes into account the performance of individual units as part of the mix which ultimately supplies the campus and determine the degree that each should be operating to most efficiently meet demand. The algorithm is part of an integrated simulation tool which is specifically designed to apply traditional optimization techniques for a given (both current and possible) circumstance. The second component is to couple the algorithm with accurate estimates and historical data through which expected demand could be predicted. The simulation tool can account for any theoretical circumstance, which will be highly beneficial for strategic planning. As part of the process it is also necessary to determine the unique operating characteristics of the system components. The algorithms rely upon performance curves of individual system components (boiler, chiller, etc.) and those must be developed and refined when possible from experimental testing and commissioning or manufacturer supplied data.
Optimal dynamic remapping of parallel computations
NASA Technical Reports Server (NTRS)
Nicol, David M.; Reynolds, Paul F., Jr.
1987-01-01
A large class of computations are characterized by a sequence of phases, with phase changes occurring unpredictably. The decision problem was considered regarding the remapping of workload to processors in a parallel computation when the utility of remapping and the future behavior of the workload is uncertain, and phases exhibit stable execution requirements during a given phase, but requirements may change radically between phases. For these problems a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The fundamental problem of balancing the expected remapping performance gain against the delay cost was addressed. Stochastic dynamic programming is used to show that the remapping decision policy minimizing the expected running time of the computation has an extremely simple structure. Because the gain may not be predictable, the performance of a heuristic policy that does not require estimnation of the gain is examined. The heuristic method's feasibility is demonstrated by its use on an adaptive fluid dynamics code on a multiprocessor. The results suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change. The results also suggest that this heuristic is applicable to computations with more than two phases.
Structural dynamics test simulation and optimization for aerospace components
Klenke, S.E.; Baca, T.J.
1996-06-01
This paper initially describes an innovative approach to product realization called Knowledge Based Testing (KBT). This research program integrates test simulation and optimization software, rapid fabrication techniques and computational model validation to support a new experimentally-based design concept. This design concept implements well defined tests earlier in the design cycle enabling the realization of highly reliable aerospace components. A test simulation and optimization software environment provides engineers with an essential tool needed to support this KBT approach. This software environment, called the Virtual Environment for Test Optimization (VETO), integrates analysis and test based models to support optimal structural dynamic test design. A goal in developing this software tool is to provide test and analysis engineers with a capability of mathematically simulating the complete structural dynamics test environment within a computer. A developed computational model of an aerospace component can be combined with analytical and/or experimentally derived models of typical structural dynamic test instrumentation within the VETO to determine an optimal test design. The VETO provides the user with a unique analysis and visualization environment to evaluate new and existing test methods in addition to simulating specific experiments designed to maximize test based information needed to validate computational models. The results of both a modal and a vibration test design are presented for a reentry vehicle and a space truss structure.
Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels
NASA Technical Reports Server (NTRS)
Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.
2011-01-01
We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols B. M. Adams 1 , H. T. Banks in model- ing HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical
Dynamical Systems, Optimization, and Chaos John B. Moore
Moore, John Barratt
and Engineering Canberra ACT 0200, Australia Abstract Much of engineering is concerned with the topic to the topic of optimization of systems via dynamical systems, where traditionally chaos is avoided as much of drugs for anesthesia, epilepsy and other conditions. A key question of interest is: Are the underlying
PARKING GARAGES WITH OPTIMAL DYNAMICS MEITAL COHEN AND BARAK WEISS
Weiss, Barak
PARKING GARAGES WITH OPTIMAL DYNAMICS MEITAL COHEN AND BARAK WEISS Abstract. We construct generalized polygons (`parking garages') in which the billiard flow satisfies the Veech dichotomy, although and Statement of results A parking garage is an immersion h : N R2 , where N is a two dimensional compact
Optimization of designed armadillo repeat proteins by molecular dynamics
Caflisch, Amedeo
Optimization of designed armadillo repeat proteins by molecular dynamics simulations and NMR the thermodynamic stability of armadillo repeat proteins (ArmRPs). ArmRPs can form the basis of modular peptide-naphthalene sulfonate; ArmRP, Armadillo Repeat Protein; CD, circular dichro- ism; GdnHCl, guanidine
Circuit Optimization Predicts Dynamic Networks for Chemosensory Orientation in the
Conery, John
Circuit Optimization Predicts Dynamic Networks for Chemosensory Orientation in the Nematode Eugene, OR 97403 shawn@lox.uoregon.edu Abstract The connectivity of the nervous system of the nematode suffi- cient to compute the sensorimotor transformation underlying C. elegans chemotaxis, a simple form
Dynamic optimization of a plate reactor start-up
Dynamic optimization of a plate reactor start-up Staffan Haugwitz, Per Hagander and John Bagterp Jørgensen Lund-Lyngby-Ålborg-dagen 061101 Staffan Haugwitz et al Control of a plate reactor #12;Process configurations : 2 inj. / 1 cool zone T T T T T T T T T T Reactor outletReactant A Reactant B Cooling water uB1 u
Optimal arrival tra c spacing via dynamic programming
Alexandre M. Bayen; Todd Callantine; Claire J. Tomlin; Yinyu Ye; Jiawei Zhang
We present the application of dynamic programming to a combinatorial optimization problem to achieve proper arrival runway spacing, which appears in the process of assign-ing speed during the transition to approach and approach phases of flight. We apply the algorithm to data from a fast-time simulation developed under NASA's Advanced Air Transportation Technologies Project for investigating new air tra c
Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation
Goetz, Stefan M.; Truong, Cong Nam; Gerhofer, Manuel G.; Peterchev, Angel V.; Herzog, Hans-Georg; Weyh, Thomas
2013-01-01
Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses. PMID:23469168
Neural dynamic optimization for control systems.III. Applications.
Seong, C Y; Widrow, B
2001-01-01
For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively. PMID:18244817
Context Aware Dynamic Traffic Signal Optimization
NASA Astrophysics Data System (ADS)
Khandwala, Kandarp; Sharma, Rudra; Rao, Snehal
2014-08-01
Conventional urban traffic control systems have been based on historical traffic data. Later advancements made use of detectors, which enabled the gathering of real time traffic data, in order to reorganize and calibrate traffic signalization programs. Further evolvement provided the ability to forecast traffic conditions, in order to develop traffic signalization programs and strategies precomputed and applied at the most appropriate time frame for the optimal control of the current traffic conditions. We, propose the next generation of traffic control systems based on principles of Artificial Intelligence and Context Awareness. Most of the existing algorithms use average waiting time or length of the queue to assess an algorithms performance. However, a low average waiting time may come at the cost of delaying other vehicles indefinitely. In our algorithm, besides the vehicle queue, we use fairness also as an important performance metric to assess an algorithms performance.
Chvátal Closures for mixed Integer Programming Problems
William J. Cook; Ravi Kannan; Alexander Schrijver
1990-01-01
Chvátal introduced the idea of viewing cutting planes as a system for proving that every integral solution of a given set of linear inequalities satisfies another given linear inequality. This viewpoint has proven to be very useful in many studies of combinatorial and integer programming problems. The basic ingredient in these cutting-plane proofs is that for a polyhedronP and integral
Optimization Research of Generation Investment Based on Linear Programming Model
NASA Astrophysics Data System (ADS)
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
Set-valued dynamic treatment regimes for competing outcomes
Laber, Eric B.; Lizotte, Daniel J.; Ferguson, Bradley
2014-01-01
Summary Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal dynamic treatment regimes, for example Q-learning, require the specification of a single outcome by which the ‘goodness’ of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, e.g., symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing dynamic treatment regimes that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study. PMID:24400912
Experimental Testing of Dynamically Optimized Photoelectron Beams
NASA Astrophysics Data System (ADS)
Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.; Jones, S.
2006-11-01
We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.
Experimental Testing of Dynamically Optimized Photoelectron Beams
Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J. [UCLA Dept. of Physics and Astronomy, 405 Hilgard Ave., Los Angeles, CA 90034 (United States); Musumeci, P. [Istituto Nazionale di Fisica Nucleare, Sezione Roma 1, Rome (RM) (Italy); Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Vicario, C. [Istituto Nazionale di Fisica Nucleare, Laboratori Nazionale di Frascati, Frascati (RM) (Italy); Serafini, L.; Jones, S. [Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91101 (United States)
2006-11-27
We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.
Optimization and equilibrium in dynamic networks and applications in traffic systems
Lin, Maokai
2015-01-01
This thesis discusses optimization problems and equilibrium in networks. There are three major parts of the thesis. In the first part, we discuss optimization in dynamic networks. We focus on two fundamental optimization ...
Experimental Testing of Dynamically Optimized Photoelectron Beams
NASA Astrophysics Data System (ADS)
Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Pirro, G. Di; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.
2007-09-01
We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC (located at INFN-LNF, Frascati) photoinjector. The scheme under study employs the natural tendency in intense electron beams to configure themselves to produce a uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation, We show that the existing infrastructure at SPARC is nearly ideal for the proposed tests, and that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.
Experimental Testing of Dynamically Optimized Photoelectron Beams
NASA Astrophysics Data System (ADS)
Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.
We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC (located at INFN-LNF, Frascati) photoinjector. The scheme under study employs the natural tendency in intense electron beams to configure themselves to produce a uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation, We show that the existing infrastructure at SPARC is nearly ideal for the proposed tests, and that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.
Adaptive optimal spectral range for dynamically changing scene
NASA Astrophysics Data System (ADS)
Pinsky, Ephi; Siman-tov, Avihay; Peles, David
2012-06-01
A novel multispectral video system that continuously optimizes both its spectral range channels and the exposure time of each channel autonomously, under dynamic scenes, varying from short range-clear scene to long range-poor visibility, is currently being developed. Transparency and contrast of high scattering medium of channels with spectral ranges in the near infrared is superior to the visible channels, particularly to the blue range. Longer wavelength spectral ranges that induce higher contrast are therefore favored. Images of 3 spectral channels are fused and displayed for (pseudo) color visualization, as an integrated high contrast video stream. In addition to the dynamic optimization of the spectral channels, optimal real-time exposure time is adjusted simultaneously and autonomously for each channel. A criterion of maximum average signal, derived dynamically from previous frames of the video stream is used (Patent Application - International Publication Number: WO2009/093110 A2, 30.07.2009). This configuration enables dynamic compatibility with the optimal exposure time of a dynamically changing scene. It also maximizes the signal to noise ratio and compensates each channel for the specified value of daylight reflections and sensors response for each spectral range. A possible implementation is a color video camera based on 4 synchronized, highly responsive, CCD imaging detectors, attached to a 4CCD dichroic prism and combined with a common, color corrected, lens. Principal Components Analysis (PCA) technique is then applied for real time "dimensional collapse" in color space, in order to select and fuse, for clear color visualization, the 3 most significant principal channels out of at least 4 characterized by high contrast and rich details in the image data.
Shape Optimization of Vehicle Radiator Using Computational Fluid Dynamics (cfd)
NASA Astrophysics Data System (ADS)
Maddipatla, Sridhar; Guessous, Laila
2002-11-01
Automotive manufacturers need to improve the efficiency and lifetime of all engine components. In the case of radiators, performance depends significantly on coolant flow homogeneity across the tubes and overall pressure drop between the inlet and outlet. Design improvements are especially needed in tube-flow uniformity to prevent premature fouling and failure of heat exchangers. Rather than relying on ad-hoc geometry changes, the current study combines Computational Fluid Dynamics with shape optimization methods to improve radiator performance. The goal is to develop an automated suite of virtual tools to assist in radiator design. Two objective functions are considered: a flow non-uniformity coefficient,Cf, and the overall pressure drop, dP*. The methodology used to automate the CFD and shape optimization procedures is discussed. In the first phase, single and multi-variable optimization methods, coupled with CFD, are applied to simplified 2-D radiator models to investigate effects of inlet and outlet positions on the above functions. The second phase concentrates on CFD simulations of a simplified 3-D radiator model. The results, which show possible improvements in both pressure and flow uniformity, validate the optimization criteria that were developed, as well as the potential of shape optimization methods with CFD to improve heat exchanger design. * Improving Radiator Design Through Shape Optimization, L. Guessous and S. Maddipatla, Paper # IMECE2002-33888, Proceedings of the 2002 ASME International Mechanical Engineering Congress and Exposition, November 2002
Solving Convex MINLP Optimization Problems Using a Sequential Cutting Plane Algorithm
Claus Still; Tapio Westerlund
2006-01-01
In this article we look at a new algorithm for solving convex mixed integer nonlinear programming problems. The algorithm\\u000a uses an integrated approach, where a branch and bound strategy is mixed with solving nonlinear programming problems at each\\u000a node of the tree. The nonlinear programming problems, at each node, are not solved to optimality, rather one iteration step\\u000a is taken
Vasco M. Manquinho; João P. Marques Silva
2005-01-01
Abstract Cutting planes are a well-known, widely used, and very effective technique for Integer Linear Programming,(ILP). In contrast, the utilization of cutting planes in Pseudo- Boolean Optimization (PBO) is recent and results still pre- liminary. This paper addresses the utilization of cutting planes, namely Gomory mixed-integer cuts, in Satisability- based algorithms for PBO, and shows how these cuts can be
A Lane-Based Optimization Method for Minimizing Delay at Isolated Signal-Controlled Junctions
C. K. Wong; S. C. Wong
2003-01-01
This paper presents a lane-based optimization method for minimizing delay at isolated signal-controlled junctions. The method\\u000a integrates the design of lane markings and signal settings, and considers both traffic and pedestrian movements in a unified\\u000a framework. While the capacity maximization and cycle length minimization problems are formulated as Binary-Mix-Integer-Linear-Programs\\u000a (BMILPs) that are solvable by standard branch-and-bound routines, the problem of
Airframe structural dynamic considerations in rotor design optimization
NASA Technical Reports Server (NTRS)
Kvaternik, Raymond G.; Murthy, T. Sreekanta
1989-01-01
An an overview and discussion of those aspects of airframe structural dynamics that have a strong influence on rotor design optimization is provided. Primary emphasis is on vibration requirements. The vibration problem is described, the key vibratory forces are identified, the role of airframe response in rotor design is summarized, and the types of constraints which need to be imposed on rotor design due to airframe dynamics are discussed. Some considerations of ground and air resonance as they might affect rotor design are included.
Tensor-optimized antisymmetrized molecular dynamics in nuclear physics
NASA Astrophysics Data System (ADS)
Myo, Takayuki; Toki, Hiroshi; Ikeda, Kiyomi; Horiuchi, Hisashi; Suhara, Tadahiro
2015-07-01
We develop a new formalism to treat nuclear many-body systems using the bare nucleon-nucleon interaction. It has become evident that the tensor interaction plays an important role in nuclear many-body systems due to the role of the pion in strongly interacting systems. We take the antisymmetrized molecular dynamics (AMD) as a basic framework and add a tensor correlation operator acting on the AMD wave function using the concept of the tensor-optimized shell model. We demonstrate a systematical and straightforward formulation utilizing the Gaussian integration and differentiation method and the antisymmetrization technique to calculate all the matrix elements of the many-body Hamiltonian. We can include the three-body interaction naturally and calculate the matrix elements systematically in the progressive order of the tensor correlation operator. We call the new formalism "tensor-optimized antisymmetrized molecular dynamics".
A Dynamically Optimized SSVEP Brain-Computer Interface (BCI) Speller.
Yin, Erwei; Zhou, Zongtan; Jiang, Jun; Yu, Yang; Hu, Dewen
2015-06-01
The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications. PMID:24801483
Adapting Genetic Algorithms for Combinatorial Optimization Problems in Dynamic Environments
Abdunnaser Younes; Shawki Areibi; Paul Calamai; Otman Basir
2008-01-01
Combinatorial optimization problems (COPs) have a wide range of applications in engineering, operation research, and social sciences. Moreover, as real-time information and communication systems become increasingly available and the processing of real-time data becomes increasingly affordable, new versions of highly dynamic real-world applications are created. In such applications, information on the problem is not completely known a priori, but instead
Confronting dynamics and uncertainty in optimal decision making for conservation
Williams, Byron K.; Johnson, Fred A.
2013-01-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.
Confronting dynamics and uncertainty in optimal decision making for conservation
NASA Astrophysics Data System (ADS)
Williams, Byron K.; Johnson, Fred A.
2013-06-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.
Rethinking design parameters in the search for optimal dynamic seating.
Pynt, Jennifer
2015-04-01
Dynamic seating design purports to lessen damage incurred during sedentary occupations by increasing sitter movement while modifying muscle activity. Dynamic sitting is currently defined by O'Sullivan et al. ( 2013a) as relating to 'the increased motion in sitting which is facilitated by the use of specific chairs or equipment' (p. 628). Yet the evidence is conflicting that dynamic seating creates variation in the sitter's lumbar posture or muscle activity with the overall consensus being that current dynamic seating design fails to fulfill its goals. Research is needed to determine if a new generation of chairs requiring active sitter involvement fulfills the goals of dynamic seating and aids cardio/metabolic health. This paper summarises the pursuit of knowledge regarding optimal seated spinal posture and seating design. Four new forms of dynamic seating encouraging active sitting are discussed. These are 1) The Core-flex with a split seatpan to facilitate a walking action while seated 2) the Duo balans requiring body action to create rocking 3) the Back App and 4) Locus pedestal stools both using the sitter's legs to drive movement. Unsubstantiated claims made by the designers of these new forms of dynamic seating are outlined. Avenues of research are suggested to validate designer claims and investigate whether these designs fulfill the goals of dynamic seating and assist cardio/metabolic health. Should these claims be efficacious then a new definition of dynamic sitting is suggested; 'Sitting in which the action is provided by the sitter, while the dynamic mechanism of the chair accommodates that action'. PMID:25892386
NASA Astrophysics Data System (ADS)
St. Germain, Brad David
The development and optimization of liquid rocket engines is an integral part of space vehicle design, since most Earth-to-orbit launch vehicles to date have used liquid rockets as their main propulsion system. Rocket engine design tools range in fidelity from very simple conceptual level tools to full computational fluid dynamics (CFD) simulations. The level of fidelity of interest in this research is a design tool that determines engine thrust and specific impulse as well as models the powerhead of the engine. This is the highest level of fidelity applicable to a conceptual level design environment where faster running analyses are desired. The optimization of liquid rocket engines using a powerhead analysis tool is a difficult problem, because it involves both continuous and discrete inputs as well as a nonlinear design space. Example continuous inputs are the main combustion chamber pressure, nozzle area ratio, engine mixture ratio, and desired thrust. Example discrete variable inputs are the engine cycle (staged-combustion, gas generator, etc.), fuel/oxidizer combination, and engine material choices. Nonlinear optimization problems involving both continuous and discrete inputs are referred to as Mixed-Integer Nonlinear Programming (MINLP) problems. Many methods exist in literature for solving MINLP problems; however none are applicable for this research. All of the existing MINLP methods require the relaxation of the discrete variables as part of their analysis procedure. This means that the discrete choices must be evaluated at non-discrete values. This is not possible with an engine powerhead design code. Therefore, a new optimization method was developed that uses modified response surface equations to provide lower bounds of the continuous design space for each unique discrete variable combination. These lower bounds are then used to efficiently solve the optimization problem. The new optimization procedure was used to find optimal rocket engine designs subject to various weight, cost and performance constraints. The results show that the new method efficiently solved the mixed-input optimization problem without requiring discrete variable relaxation.
Optimization of Dynamic Aperture of PEP-X Baseline Design
Wang, Min-Huey; Cai, Yunhai; Nosochkov, Yuri; ,
2010-08-23
SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-{angstrom} x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A; Akram, M Usman; Jamal, Habib Ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A.; Akram, M. Usman; Jamal, Habib ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Human opinion dynamics: an inspiration to solve complex optimization problems.
Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P; Kapur, Pawan
2013-01-01
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795
Optimal forwarding ratio on dynamical networks with heterogeneous mobility
NASA Astrophysics Data System (ADS)
Gan, Yu; Tang, Ming; Yang, Hanxin
2013-05-01
Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.
Human opinion dynamics: An inspiration to solve complex optimization problems
Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan
2013-01-01
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795
Optimizing spread dynamics on graphs by message passing
NASA Astrophysics Data System (ADS)
Altarelli, F.; Braunstein, A.; Dall'Asta, L.; Zecchina, R.
2013-09-01
Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the past decades, much effort has been devoted to understanding the typical behavior of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception being models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network).
Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework
J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto
2014-06-01
Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.
A mathematical programming approach to stochastic and dynamic optimization problems
Bertsimas, D.
1994-12-31
We propose three ideas for constructing optimal or near-optimal policies: (1) for systems for which we have an exact characterization of the performance space we outline an adaptive greedy algorithm that gives rise to indexing policies (we illustrate this technique in the context of indexable systems); (2) we use integer programming to construct policies from the underlying descriptions of the performance space (we illustrate this technique in the context of polling systems); (3) we use linear control over polyhedral regions to solve deterministic versions for this class of problems. This approach gives interesting insights for the structure of the optimal policy (we illustrate this idea in the context of multiclass queueing networks). The unifying theme in the paper is the thesis that better formulations lead to deeper understanding and better solution methods. Overall the proposed approach for stochastic and dynamic optimization parallels efforts of the mathematical programming community in the last fifteen years to develop sharper formulations (polyhedral combinatorics and more recently nonlinear relaxations) and leads to new insights ranging from a complete characterization and new algorithms for indexable systems to tight lower bounds and new algorithms with provable a posteriori guarantees for their suboptimality for polling systems, multiclass queueing and loss networks.
Modelling the Transmission Dynamics of Hepatitis B & Optimal control
Mehmood, Nayyar
2011-01-01
Hepatitis B is a potentially life-threatening viral infection that can cause illness and even death. The Hepatitis B virus is fifty to hundred times more infectious than HIV, and world wide an estimated two billion people have been affected (WHO2008). HBV is the most common serious viral infection and leading cause of deaths in main land in china South Asia and Africa and almost all parts of the world. Every year 300,000 people from HBV related diseases only in China. Hepatitis B is a common disease in Pakistan as every tenth person is carrier and every fifth person has been exposed to hepatitis B. We proposed a model to understand the transmission dynamics and prevalence of HBV. To control the prevalence of diseases, we use optimal control strategies, which reduce the latent, infected and carrier individuals and increase the total number of recovered and immune individuals. In order to do this, we show that an optimal control exists for this optimal control problem and then we use optimal control techniques ...
Dynamics-aware Optimal Power Flow Enrique Mallada and Ao Tang
Tang, A. Kevin
Dynamics-aware Optimal Power Flow Enrique Mallada and Ao Tang Abstract-- The development of open. In this paper we present an optimal power flow formulation that aims to close this gap. First, we show to generate a dynamics-aware optimal power flow formulation that provides voltage as well as small signal
Code Cache Management Schemes for Dynamic Optimizers Kim Hazelwood Michael D. Smith
Hazelwood, Kim
Code Cache Management Schemes for Dynamic Optimizers Kim Hazelwood Michael D. Smith Division optimizer is a software-based system that performs code modifications at runtime, and several such systems of code. Since the dynamic optimizers produce variable-length code traces that are modified copies
Exposure time optimization for highly dynamic star trackers.
Wei, Xinguo; Tan, Wei; Li, Jian; Zhang, Guangjun
2014-01-01
Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers. PMID:24618776
Optimal approach to quantum communication using dynamic programming.
Jiang, Liang; Taylor, Jacob M; Khaneja, Navin; Lukin, Mikhail D
2007-10-30
Reliable preparation of entanglement between distant systems is an outstanding problem in quantum information science and quantum communication. In practice, this has to be accomplished by noisy channels (such as optical fibers) that generally result in exponential attenuation of quantum signals at large distances. A special class of quantum error correction protocols, quantum repeater protocols, can be used to overcome such losses. In this work, we introduce a method for systematically optimizing existing protocols and developing more efficient protocols. Our approach makes use of a dynamic programming-based searching algorithm, the complexity of which scales only polynomially with the communication distance, letting us efficiently determine near-optimal solutions. We find significant improvements in both the speed and the final-state fidelity for preparing long-distance entangled states. PMID:17959783
The role of controllability in optimizing quantum dynamics
Wu, Re-Bing; Rabitz, Herschel
2009-01-01
This paper discusses the important role of controllability played on the complexity of optimizing quantum mechanical control systems. The study is based on a topology analysis of the corresponding quantum control landscape, which is referred to as the optimization objective as a functional of control fields. We find that the degree of controllability is closely relevant with the ruggedness of the landscape, which determines the search efficiency for global optima. This effect is demonstrated via the gate fidelity control landscape of a system whose controllability is restricted on a SU(2) dynamic symmetry group. We show that multiple local false traps (i.e., non-global suboptima) exist even if the target gate is realizable and that the number of these traps is increased by the loss of controllability, while the controllable systems are always devoid of false traps.
The role of controllability in optimizing quantum dynamics
Re-Bing Wu; Michael A. Hsieh; Herschel Rabitz
2011-01-06
This paper discusses the important role of controllability played on the complexity of optimizing quantum mechanical control systems. The study is based on a topology analysis of the corresponding quantum control landscape, which is referred to as the optimization objective as a functional of control fields. We find that the degree of controllability is closely relevant with the ruggedness of the landscape, which determines the search efficiency for global optima. This effect is demonstrated via the gate fidelity control landscape of a system whose controllability is restricted on a SU(2) dynamic symmetry group. We show that multiple local false traps (i.e., non-global suboptima) exist even if the target gate is realizable and that the number of these traps is increased by the loss of controllability, while the controllable systems are always devoid of false traps.
Dynamics of Two Qubits: Decoherence and an Entanglement Optimization Protocol
Cesar A. Rodriguez; Anil Shaji; E. C. G. Sudarshan
2006-11-10
The evolution of two qubits coupled by a general nonlocal interaction is studied in two distinct regimes. In the first regime the purity of the individual qubits is interchanged through the entanglement shared by the two. We illustrate how this can be a mechanism for decoherence. In the second regime, the interaction entangles two initially pure qubits. The dynamical maps for the reduced unitary evolution of both initially simply separable and not-simply-separable states are found. We outline a protocol for optimizing the entanglement generation subject to constraints.
Optimized dynamical decoupling for power-law noise spectra
Pasini, S.; Uhrig, G. S. [Lehrstuhl fuer Theoretische Physik I, TU Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany)
2010-01-15
We analyze the suppression of decoherence by means of dynamical decoupling in the pure-dephasing spin-boson model for baths with power law spectra. The sequence of ideal pi pulses is optimized according to the power of the bath. We expand the decoherence function and separate the canceling divergences from the relevant terms. The proposed sequence is chosen to be the one minimizing the decoherence function. By construction, it provides the best performance. We analytically derive the conditions that must be satisfied. The resulting equations are solved numerically. The solutions are very close to the Carr-Purcell-Meiboom-Gill sequence for a soft cutoff of the bath while they approach the Uhrig dynamical-decoupling sequence as the cutoff becomes harder.
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944
Clustering molecular dynamics trajectories for optimizing docking experiments.
De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944
Aircraft path planning for optimal imaging using dynamic cost functions
NASA Astrophysics Data System (ADS)
Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin
2015-05-01
Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.
Optimizing dynamical similarity index extraction window for seizure detection.
Azinfar, Leila; Rabbi, Ahmed; Ravanfar, Mohammdreza; Noghanian, Sima; Fazel-Rezai, Reza
2014-01-01
This paper addresses an optimization problem in choosing optimum window length for feature extraction in automatic seizure detection. The processing window length plays an important role in reducing the false positive and false negative rates and decreasing required processing time for seizure detection. This study presents an approach for selecting the optimum window length toward the extraction of dynamical similarity index (DSI) feature. Then, the optimal window value in DSI extraction was used to detect seizure onset automatically. The algorithm was applied to electroencephalogram (EEG) signals from European Epilepsy Database. Although the main purpose of this study was not the seizure detection and mainly focuses on proposing an approach for finding an optimum window length for feature extraction towards the early seizure detection, the results showed that the proposed method achieves 83.99% of sensitivity in seizure detection. The low false positive rate per hour (FPR/h) was also significant due to continuous EEG analysis. The method showed fast computation speed which promises a potential for the real time applications. The proposed method for the window optimization in feature extraction of DSI can be implemented for other features to further improve the performance of seizure detection. PMID:25570429
Data-driven optimization of dynamic reconfigurable systems of systems.
Tucker, Conrad S.; Eddy, John P.
2010-11-01
This report documents the results of a Strategic Partnership (aka University Collaboration) LDRD program between Sandia National Laboratories and the University of Illinois at Urbana-Champagne. The project is titled 'Data-Driven Optimization of Dynamic Reconfigurable Systems of Systems' and was conducted during FY 2009 and FY 2010. The purpose of this study was to determine and implement ways to incorporate real-time data mining and information discovery into existing Systems of Systems (SoS) modeling capabilities. Current SoS modeling is typically conducted in an iterative manner in which replications are carried out in order to quantify variation in the simulation results. The expense of many replications for large simulations, especially when considering the need for optimization, sensitivity analysis, and uncertainty quantification, can be prohibitive. In addition, extracting useful information from the resulting large datasets is a challenging task. This work demonstrates methods of identifying trends and other forms of information in datasets that can be used on a wide range of applications such as quantifying the strength of various inputs on outputs, identifying the sources of variation in the simulation, and potentially steering an optimization process for improved efficiency.
Optimal control and cold war dynamics between plant and herbivore.
Low, Candace; Ellner, Stephen P; Holden, Matthew H
2013-08-01
Herbivores eat the leaves that a plant needs for photosynthesis. However, the degree of antagonism between plant and herbivore may depend critically on the timing of their interactions and the intrinsic value of a leaf. We present a model that investigates whether and when the timing of plant defense and herbivore feeding activity can be optimized by evolution so that their interactions can move from antagonistic to neutral. We assume that temporal changes in environmental conditions will affect intrinsic leaf value, measured as potential carbon gain. Using optimal-control theory, we model herbivore evolution, first in response to fixed plant strategies and then under coevolutionary dynamics in which the plant also evolves in response to the herbivore. In the latter case, we solve for the evolutionarily stable strategies of plant defense induction and herbivore hatching rate under different ecological conditions. Our results suggest that the optimal strategies for both plant and herbivore are to avoid direct conflict. As long as the plant has the capability for moderately lethal defense, the herbivore will modify its hatching rate to avoid plant defenses, and the plant will never have to use them. Insights from this model offer a possible solution to the paradox of sublethal defenses and provide a mechanism for stable plant-herbivore interactions without the need for natural enemy control. PMID:23852361
An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1992-01-01
The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.
Optimal spatiotemporal reduced order modeling for nonlinear dynamical systems
NASA Astrophysics Data System (ADS)
LaBryer, Allen
Proposed in this dissertation is a novel reduced order modeling (ROM) framework called optimal spatiotemporal reduced order modeling (OPSTROM) for nonlinear dynamical systems. The OPSTROM approach is a data-driven methodology for the synthesis of multiscale reduced order models (ROMs) which can be used to enhance the efficiency and reliability of under-resolved simulations for nonlinear dynamical systems. In the context of nonlinear continuum dynamics, the OPSTROM approach relies on the concept of embedding subgrid-scale models into the governing equations in order to account for the effects due to unresolved spatial and temporal scales. Traditional ROMs neglect these effects, whereas most other multiscale ROMs account for these effects in ways that are inconsistent with the underlying spatiotemporal statistical structure of the nonlinear dynamical system. The OPSTROM framework presented in this dissertation begins with a general system of partial differential equations, which are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which residual terms, representing subgrid-scale dynamics, arise with a coarse computational grid. Models for these residual terms are then developed by accounting for the underlying spatiotemporal statistical structure in a consistent manner. These subgrid-scale models are designed to provide closure by accounting for the dynamic interactions between spatiotemporal macroscales and microscales which are otherwise neglected in a ROM. For a given resolution, the predictions obtained with the modified system of equations are optimal (in a mean-square sense) as the subgrid-scale models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. Methods are suggested for efficient model construction, appraisal, error measure, and implementation with a couple of well-known time-discretization schemes. Four nonlinear dynamical systems serve as testbeds to demonstrate the technique. First we consider an autonomous van der Pol oscillator for which all trajectories evolve to self-sustained limit cycle oscillations. Next we investigate a forced Duffing oscillator for which the response may be regular or chaotic. In order to demonstrate application for a problem in nonlinear wave propagation, we consider the viscous Burgers equation with large-amplitude inflow disturbances. For the fourth and final system, we analyze the nonlinear structural dynamics of a geometrically nonlinear beam under the influence of time-dependent external forcing. The practical utility of the proposed subgrid-scale models is enhanced if it can be shown that certain statistical moments amongst the subgrid-scale dynamics display to some extent the following properties: spatiotemporal homogeneity, ergodicity, smooth scaling with respect to the system parameters, and universality. To this end, we characterize the subgrid-scale dynamics for each of the four problems. The results in this dissertation indicate that temporal homogeneity and ergodicity are excellent assumptions for both regular and chaotic response types. Spatial homogeneity is found to be a very good assumption for the nonlinear beam problem with models based upon single-point but not multi-point spatial stencils. The viscous Burgers flow, however, requires spatially heterogeneous models regardless of the stencil. For each of the four problems, the required statistical moments display a functional dependence which can easily be characterized with respect to the physical parameters and the computational grid. This observed property, in particular, greatly simplifies model construction by way of moment estimation. We investigate the performance of the subgrid-scale models with under-resolved simulations (in space and time) and various discretization schemes. For the canonical Duffing and van der Pol oscillators, the subgrid-scale models are found to improve the accuracy of under-resolved time-marching and time-s
Deshmukh, Venkatesh
Dynamical Systems Venkatesh Deshmukh Center for Nonlinear Dynamics and Control Department of MechanicalSpectral Collocation-based Optimization in Parameter Estimation for Nonlinear Time- Varying collocation and quadratic programming is proposed for unknown parameter estimation in nonlinear time- varying
Sonic Millip3De with Dynamic Receive Focusing and Apodization Optimization
Wenisch, Thomas F.
Sonic Millip3De with Dynamic Receive Focusing and Apodization Optimization Richard Sampson, Ming with a new sub-aperture firing scheme and dynamic focus apodization that allows the system to generate images
Performance Study and Dynamic Optimization Design for Thread Pool Systems
Dongping Xu
2004-12-19
Thread pools have been widely used by many multithreaded applications. However, the determination of the pool size according to the application behavior still remains problematic. To automate this process, in this thesis we have developed a set of performance metrics for quantitatively analyzing thread pool performance. For our experiments, we built a thread pool system which provides a general framework for thread pool research. Based on this simulation environment, we studied the performance impact brought by the thread pool on different multithreaded applications. Additionally, the correlations between internal characterizations of thread pools and their throughput were also examined. We then proposed and evaluated a heuristic algorithm to dynamically determine the optimal thread pool size. The simulation results show that this approach is effective in improving overall application performance.
Optimal reconstruction of dynamical systems: A noise amplification approach
L. C. Uzal; G. L. Grinblat; P. F. Verdes
2012-05-14
In this work we propose an objective function to guide the search for a state space reconstruction of a dynamical system from a time series of measurements. This statistics can be evaluated on any reconstructed attractor, thereby allowing a direct comparison among different approaches: (uniform or non-uniform) delay vectors, PCA, Legendre coordinates, etc. It can also be used to select the most appropriate parameters of a reconstruction strategy. In the case of delay coordinates this translates into finding the optimal delay time and embedding dimension from the absolute minimum of the advocated cost function. Its definition is based on theoretical arguments on noise amplification, the complexity of the reconstructed attractor and a direct measure of local stretch which constitutes a novel irrelevance measure. The proposed method is demonstrated on synthetic and experimental time series.
Nonlinear dynamic analysis of an optimal particle damper
Martín Sánchez; C. Manuel Carlevaro
2011-10-12
We study the dynamical behavior of a single degree of freedom mechanical system with a particle damper. The particle (granular) damping was optimized for the primary system operating condition by using an appropriate gap size for a prismatic enclosure. The particles absorb the kinetic energy of the vibrating structure and convert it into heat through the inelastic collisions and friction. This results in a highly nonlinear mechanical system. Considering linear signal analysis, state space reconstruction, Poincar\\'e sections and the determination of maximal Lyapunov exponents, the motion of the granular system inside the enclosure is characterized for a wide frequency range. With the excitation frequency as control parameter, either regular and chaotic motion of the granular bed are found and their influence on the damping is analyzed.
A relaxed reduced space SQP strategy for dynamic optimization problems.
Logsdon, J. S.; Biegler, L. T.; Carnegie-Mellon Univ.
1993-01-01
Recently, strategies have been developed to solve dynamic simulation and optimization problems in a simultaneous manner by applying orthogonal collocation on finite elements and solving the nonlinear program (NLP) with a reduced space successive quadratic programming (SQP) approach. We develop a relaxed simultaneous approach that leads to faster performance. The method operates in the reduced space of the control variables and solves the collocation equations inexactly at each SQP iteration. Unlike previous simultaneous formulations, it is able to consider the state variables one element at a time. Also, this approach is compared on two process examples to the reduced gradient, feasible path approach outlined in Logsdon and Biegler. Nonlinear programs with up to 5500 variables are solved with only 40% of the effort. Finally, a theoretical analysis of this approach is provided.
Improved self-protection using dynamically optimized expendable countermeasures
NASA Astrophysics Data System (ADS)
Hovland, Harald
2007-04-01
The use of expendable countermeasures is still found to be a viable choice for self protection against Man Portable Air Defense Systems (MANPADS) due to their simplicity, low cost, flexibility, recent improvements in decoy technology, the ability to handle multiple threats simultaneously and the off-board nature of these countermeasures. In civil aviation, the risk of general hazards linked to the use of pyrotechnics is the main argument against expendable countermeasures, whereas for military platforms, the limitation in capacity due to a limited number of rounds is often used as an argument to replace expendable countermeasures by laser-based countermeasures. This latter argument is in general not substantiated by modelling or figures of merit, although it is often argued that a laser based system allows for more false alarms, hence enabling a more sensitive missile approach warning system. The author has developed a model that accounts for the statistical effects of running out of expendable countermeasures during a mission, in terms of the overall mission survival probability. The model includes key parameters of the missile approach warning system (MAWS), and can handle multiple missile types and missile attack configurations, as well as various statistical models of missile attacks. The model enables quantitative comparison between laser based and expendable countermeasures, but also a dynamic optimization of the countermeasures in terms of whether to use small or large countermeasure programs, as well as the dynamic tuning of MAWS key parameters to optimize the overall performance. The model is also well suited for determination of the contributions of the different components of the system in the overall survival probability.
Geometry optimization for micro-pressure sensor considering dynamic interference
Yu, Zhongliang; Zhao, Yulong Li, Lili; Tian, Bian; Li, Cun
2014-09-15
Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz{sup 1/4}. The favorable overall performances enable the sensor more suitable for altimetry.
Dynamic online optimization of a house heating system in a fluctuating energy price
Skogestad, Sigurd
Dynamic online optimization of a house heating system in a fluctuating energy price scenario in this problem is the time- varying nature of the main disturbances, which are the energy price and outdoor that there is a great economical gain in using dynamic optimization for the case of variable energy price. 1
One-Dimensional Infinite Horizon Nonconcave Optimal Control Problems Arising in Economic Dynamics
Zaslavski, Alexander J.
2011-12-15
We study the existence of optimal solutions for a class of infinite horizon nonconvex autonomous discrete-time optimal control problems. This class contains optimal control problems without discounting arising in economic dynamics which describe a model with a nonconcave utility function.
Design of penalty functions for optimal control of linear dynamical systems under state and input of solving a constrained optimal control for a general single-input single output linear time varying system dimensional (functional optimization) case. The main novelty is that both the bounds on the control variable
Indirect optimization of interplanetary trajectories including spiral dynamics
NASA Astrophysics Data System (ADS)
Ranieri, Christopher Louis
2007-12-01
In the future small, robotic probes and large, human-crewed spacecraft will utilize long-duration, finite-burning engines. These types of engines have already been proven in several missions and research is on-going for the design of newer engines of this class. The trajectories they generate are significantly different than those that use conventional high-thrust chemical propulsion. The indirect method was chosen for the optimization of the missions presented due to its mathematical elegance and other problem specific issues. The goal was to choose a difficult optimization problem for an engine of this class and determine techniques, trends, and formulations that help overcome the indirect method's traditional shortcoming: the numerical difficulty of generating an accurate first guess for complex missions. The problem chosen was the optimization of interplanetary trajectories, with particular results presented for the problem of transferring from Low Earth Orbit (LEO) to Low Mars Orbit (LMO). Most previous attempts at this problem use an array of simplifications to the engine system and/or problem dynamics to make the optimization feasible. These simplifications are systematically removed here. The complete trajectory was broken down into its component phases and careful study was paid to each. Analysis of the escape and capture spirals provided useful insight into the appropriate coordinate frames for spirals. This study yielded a technique that quickly and accurately estimates the unknown Lagrange multipliers. These results were applied to the full LEO to LMO mission as part of a sequential process for a two-dimensional solar system model and the equivalent three-dimensional model. This process includes many new derivations that facilitate the generation of an accurate first guess for these LEO to LMO missions. One new derivation in particular is vital where the co-states for a Mars capture spiral referenced to a Martian coordinate frame are transformed into their Earth based equivalents. This sets up a multiple shooting problem integrated in a single coordinate frame which is different than the single shooting method used in published benchmarks. The new approach generates more fuel efficient trajectories and significantly more complex numerically achievable capture sequence compared with such benchmarks.
Optimal Dynamics for Quality Control in Spatially Distributed Mitochondrial Networks
Patel, Pinkesh K.; Shirihai, Orian; Huang, Kerwyn Casey
2013-01-01
Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles. PMID:23874166
Optimal dynamics for quality control in spatially distributed mitochondrial networks.
Patel, Pinkesh K; Shirihai, Orian; Huang, Kerwyn Casey
2013-01-01
Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles. PMID:23874166
NASA Astrophysics Data System (ADS)
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Optimal foot shape for a passive dynamic biped.
Kwan, Maxine; Hubbard, Mont
2007-09-21
Passive walking dynamics describe the motion of a biped that is able to "walk" down a shallow slope without any actuation or control. Instead, the walker relies on gravitational and inertial effects to propel itself forward, exhibiting a gait quite similar to that of humans. These purely passive models depend on potential energy to overcome the energy lost when the foot impacts the ground. Previous research has demonstrated that energy loss at heel-strike can vary widely for a given speed, depending on the nature of the collision. The point of foot contact with the ground (relative to the hip) can have a significant effect: semi-circular (round) feet soften the impact, resulting in much smaller losses than point-foot walkers. Collisional losses are also lower if a single impulse is broken up into a series of smaller impulses that gradually redirect the velocity of the center of mass rather than a single abrupt impulse. Using this principle, a model was created where foot-strike occurs over two impulses, "heel-strike" and "toe-strike," representative of the initial impact of the heel and the following impact as the ball of the foot strikes the ground. Having two collisions with the flat-foot model did improve efficiency over the point-foot model. Representation of the flat-foot walker as a rimless wheel helped to explain the optimal flat-foot shape, driven by symmetry of the virtual spoke angles. The optimal long period foot shape of the simple passive walking model was not very representative of the human foot shape, although a reasonably anthropometric foot shape was predicted by the short period solution. PMID:17570405
Metamodeling and the Critic-based approach to multi-level optimization.
Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J
2012-08-01
Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. PMID:22386785
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M.
2010-01-01
In this companion article to “Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content” [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption. PMID:20405047
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M
2010-01-01
In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption. PMID:20405047
Chan, Carri W
2008-01-01
This paper presents a general class of dynamic stochastic optimization problems we refer to as Stochastic Depletion Problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as also from a theoretical perspective. As such, simple heuristics are highly desirable. We isolate two simple properties that, if satisfied by a problem within this class, guarantee that a myopic policy incurs a performance loss of at most 50 % relative to the optimal adaptive control policy for that problem. We are able to verify that these two properties are satisfied for several interesting families of stochastic depletion problems and as a consequence identify efficient near-optimal control policies for a number of interesting dynamic stochastic optimization problems.
HVS BASED HIGH DYNAMIC RANGE VIDEO COMPRESSION WITH OPTIMAL BIT-DEPTH TRANSFORMATION
Reinhard, Erik
HVS BASED HIGH DYNAMIC RANGE VIDEO COMPRESSION WITH OPTIMAL BIT-DEPTH TRANSFORMATION Yang Zhang of this colour space is that it spans a huge dynamic range, sim- ilar to the adaptation range of the HVS. However, UK Email:[Yang.Zhang, Erik.Reinhard, Dave.Bull]@bristol.ac.uk ABSTRACT High Dynamic Range (HDR
Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction
Paris-Sud XI, Université de
1 Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction Nelly Abstract--This work deals with Dynamic Positron Emission Tomography (PET) data reconstruction, considering. The effectiveness of this approach is shown with simulated dynamic PET data. Comparative results are also provided
Adjoint-based Unsteady Airfoil Design Optimization with Application to Dynamic Stall
Mavripli, Dimitri J.
Adjoint-based Unsteady Airfoil Design Optimization with Application to Dynamic Stall Karthik Mani of alleviating dynamic stall effects in helicopter rotor blades. The unsteady flow problem is simulated using such that their minimization results in allevi- ation of undesirable dynamic stall characteristics while the design inputs
Comparison of spacecraft crew escape systems through dynamic optimization
NASA Astrophysics Data System (ADS)
Hart, William G., III
Crew escape systems have been a vital component of ensuring safety onboard manned spacecraft. Although there have been only a few aborts involving their use, their operation helps decrease risk in what is known to be a hazardous field. But despite their high reliability, crew escape systems typically suffer from heavy weight, lack of control and hazardous chemical propellants. Hybrid propulsion systems could be a viable solution to all of these problems. With their inert components, ability to throttle and higher specific impulse than solids, hybrids have obtained interest in recent years. This dissertation presents a method that can be used to compare solid and hybrid propulsion systems for the crew escape systems of spacecraft. The concepts of dynamic optimization, Monte Carlo simulation and propulsion system design are combined to produce a tool which can predict the probability of survival for a given abort scenario. The method can also determine the effect of uncertain variables, such as reaction time or the payload of the vehicle, in the safety of the crew. The method is then used to compare crew escape systems for two separate vehicles: a separable crew cabin proposed for the Space Shuttle Launch Vehicle and the Launch Escape System for the Crew Exploration Vehicle scheduled to begin operation in 2012. The effects of uncertain parameters are also studied. The results show the utility of this method and the objective function, and how it could be used in the design process for future space vehicles.
Morphological Optimization of Perovskite Thin Films via Dynamic Zone Annealing
NASA Astrophysics Data System (ADS)
Sun, Yan; Wang, Kai; Gong, Xiong; Karim, Alamgir
2015-03-01
Organolead Halide Perovskites have been proved to be excellent candidates for application in low-cost high-efficient solar cells owing to their superior desired optical and electrical properties, as well as compatibility with low-temperature solution-processed manufacturing. However, most perovskites applications in photovoltaics require high quality perovskite films. Although tremendous works on tuning perovskite film morphology have been reported previously, it is still a challenge to realize high quality perovskite film with controllable film uniformity and surface coverage, neither the mechanisms in the formation of perovskite. To address the issues above, here we demonstrate the effect of Dynamic Zone Annealing (DZA) on perovskite morphologies, which is proved as an efficient method to control the structure and morphology in crystalline polymer and block copolymers. Via applying the DZA method, the mechanism in perovskite film formation is studied. Furthermore, by optimizing DZA parameter such as maximum temperature, temperature gradient and zone velocity to control dendritic morphology and the grain growth, enhanced device performance was realized eventually. Equal contribution.
Function-valued adaptive dynamics and optimal control theory.
Parvinen, Kalle; Heino, Mikko; Dieckmann, Ulf
2013-09-01
In this article we further develop the theory of adaptive dynamics of function-valued traits. Previous work has concentrated on models for which invasion fitness can be written as an integral in which the integrand for each argument value is a function of the strategy value at that argument value only. For this type of models of direct effect, singular strategies can be found using the calculus of variations, with singular strategies needing to satisfy Euler's equation with environmental feedback. In a broader, more mechanistically oriented class of models, the function-valued strategy affects a process described by differential equations, and fitness can be expressed as an integral in which the integrand for each argument value depends both on the strategy and on process variables at that argument value. In general, the calculus of variations cannot help analyzing this much broader class of models. Here we explain how to find singular strategies in this class of process-mediated models using optimal control theory. In particular, we show that singular strategies need to satisfy Pontryagin's maximum principle with environmental feedback. We demonstrate the utility of this approach by studying the evolution of strategies determining seasonal flowering schedules. PMID:22763388
Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Wang, Jian; Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Optimal GENCO bidding strategy
NASA Astrophysics Data System (ADS)
Gao, Feng
Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.
NASA Astrophysics Data System (ADS)
Lee, Dong-Kyu; Park, Sung-Soo; Shin, Soo-Mi
The goal of this study is to numerically compare solutions and algorithms determined by element- and node-wise topology optimization designs for dynamic free vibration-resistance structures. As another version in the fields of topology optimization methods, the study supports the node-based optimization rather than the classical element-based optimization comparing two methods. The terms element-and node-wise denote the usage of element and node density as design parameter, respectively. For static problems solution comparisons of the two types for SIMP topology optimization designs have already been introduced by the author(1). For dynamic topology optimization problems the objective is in general related to maximum eigenfrequency optimization subject to a given material limit since structures with a high fundamental frequency tends to be reasonable stiff for static loads. For dynamic problems SIMP material is used in this study and an implemented optimization method is the method of moving asymptotes (MMA). Numerical applications topologically maximizing the first natural eigenfrequency for dynamic concrete deep beam designs depending on element or node density verify differences of solutions and algorithms between dynamic element- and node-wise topology optimum designs.
Power flow response based dynamic topology optimization of bi-material plate Structures
NASA Astrophysics Data System (ADS)
Xue, Xiaoguang; Li, Guoxi; Xiong, Yeping; Gong, Jingzhong
2013-05-01
Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, minimization of the dynamic compliance subject to forced vibration, and minimization of the structural frequency response. A dynamic topology optimization method of bi-material plate structures is presented based on power flow analysis. Topology optimization problems formulated directly with the design objective of minimizing the power flow response are dealt with. In comparison to the displacement or velocity response, the power flow response takes not only the amplitude of force and velocity into account, but also the phase relationship of the two vector quantities. The complex expression of power flow response is derived based on time-harmonic external mechanical loading and Rayleigh damping. The mathematical formulation of topology optimization is established based on power flow response and bi-material solid isotropic material with penalization(SIMP) model. Computational optimization procedure is developed by using adjoint design sensitivity analysis and the method of moving asymptotes(MMA). Several numerical examples are presented for bi-material plate structures with different loading frequencies, which verify the feasibility and effectiveness of this method. Additionally, optimum results between topological design of minimum power flow response and minimum dynamic compliance are compared, showing that the present method has strong adaptability for structural dynamic topology optimization problems. The proposed research provides a more accurate and effective approach for dynamic topology optimization of vibrating structures.
Optimized dynamic framing for PET-based myocardial blood flow estimation
NASA Astrophysics Data System (ADS)
Kolthammer, Jeffrey A.; Muzic, Raymond F.
2013-08-01
An optimal experiment design methodology was developed to select the framing schedule to be used in dynamic positron emission tomography (PET) for estimation of myocardial blood flow using 82Rb. A compartment model and an arterial input function based on measured data were used to calculate a D-optimality criterion for a wide range of candidate framing schedules. To validate the optimality calculation, noisy time-activity curves were simulated, from which parameter values were estimated using an efficient and robust decomposition of the estimation problem. D-optimized schedules improved estimate precision compared to non-optimized schedules, including previously published schedules. To assess robustness, a range of physiologic conditions were simulated. Schedules that were optimal for one condition were nearly-optimal for others. The effect of infusion duration was investigated. Optimality was better for shorter than for longer tracer infusion durations, with the optimal schedule for the shortest infusion duration being nearly optimal for other durations. Together this suggests that a framing schedule optimized for one set of conditions will also work well for others and it is not necessary to use different schedules for different infusion durations or for rest and stress studies. The method for optimizing schedules is general and could be applied in other dynamic PET imaging studies.
An optimization model for energy generation and distribution in a dynamic facility
NASA Technical Reports Server (NTRS)
Lansing, F. L.
1981-01-01
An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.
Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems
2013-01-01
Background Model development is a key task in systems biology, which typically starts from an initial model candidate and, involving an iterative cycle of hypotheses-driven model modifications, leads to new experimentation and subsequent model identification steps. The final product of this cycle is a satisfactory refined model of the biological phenomena under study. During such iterative model development, researchers frequently propose a set of model candidates from which the best alternative must be selected. Here we consider this problem of model selection and formulate it as a simultaneous model selection and parameter identification problem. More precisely, we consider a general mixed-integer nonlinear programming (MINLP) formulation for model selection and identification, with emphasis on dynamic models consisting of sets of either ODEs (ordinary differential equations) or DAEs (differential algebraic equations). Results We solved the MINLP formulation for model selection and identification using an algorithm based on Scatter Search (SS). We illustrate the capabilities and efficiency of the proposed strategy with a case study considering the KdpD/KdpE system regulating potassium homeostasis in Escherichia coli. The proposed approach resulted in a final model that presents a better fit to the in silico generated experimental data. Conclusions The presented MINLP-based optimization approach for nested-model selection and identification is a powerful methodology for model development in systems biology. This strategy can be used to perform model selection and parameter estimation in one single step, thus greatly reducing the number of experiments and computations of traditional modeling approaches. PMID:23938131
An archived multi-objective simulated annealing for a dynamic cellular manufacturing system
NASA Astrophysics Data System (ADS)
Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza
2014-05-01
To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ?-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
Lermusiaux, Pierre F. J.
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for ...
The Role of Ocean Dynamics in the Optimal Growth of Tropical SST Anomalies
Zanna, Laure
The role of ocean dynamics in optimally exciting interannual variability of tropical sea surface temperature (SST) anomalies is investigated using an idealized-geometry ocean general circulation model. Initial temperature ...
Optimal foreign borrowing in a multisector dynamic equilibrium model for Brazil
Tourinho, Octv?io A. F.
1985-01-01
This paper shows how a dynamic multisector equilibrium model can be formulated to be able to analyze the optimal borrowing policy of a developing country. It also describes how a non-linear programming model with the ...
Metaheuristic algorithms in structural dynamics: An application of tuned mass damper optimization
NASA Astrophysics Data System (ADS)
Bekda?, Gebrail; Nigdeli, Sinan Melih
2012-09-01
Metaheuristic algorithms imitate natural phenomena in order to solve optimization problems. These algorithms are effective on the optimization of structural dynamics problems including vibration control with tuned mass damper (TMD). In this paper, structural dynamics optimization problems were briefly reviewed. As an example, a TMD optimization problem was presented. Harmony Search (HS) algorithm was used to find optimum parameters of TMD mass, stiffness and damping coefficient. The optimization process was conducted to reduce structural displacements of a five story structure. The properties of the structure are the same for all stories except the third story mass. According to the analyses results, the TMD optimized with HS approach is effective to reduce all maximum story displacements.
An inverse dynamics approach to trajectory optimization for an aerospace plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1992-01-01
An inverse dynamics approach for trajectory optimization is proposed. This technique can be useful in many difficult trajectory optimization and control problems. The application of the approach is exemplified by ascent trajectory optimization for an aerospace plane. Both minimum-fuel and minimax types of performance indices are considered. When rocket augmentation is available for ascent, it is shown that accurate orbital insertion can be achieved through the inverse control of the rocket in the presence of disturbances.
Dynamic optimization of hybridoma growth in a fed-batch bioreactor.
Dhir, S; Morrow, K J; Rhinehart, R R; Wiesner, T
2000-01-20
This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process-model mismatch at each sampling time, updated the model parameters, and re-optimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single offline optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization online increased yield of cell mass per batch by 44% over the single offline optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture. PMID:10592517
Luc Wismans; Eric van Berkum; Michiel Bliemer
2012-01-01
Optimization of traffic network performance using dynamic traffic management (DTM) measures can be viewed as a specific example of solving a network design problem (NDP). Decision variables are the specific settings of DTM measures. DTM measures have been identified as not only powerful instruments to increase network efficiency, but also to improve externalities. As a result, in the optimization the
Hespanha, João Pedro
1 Stochastic Optimal Coordination of Small UAVs for Target Tracking using Regression-based Dynamic of optimally coordinating multiple fixed-wing UAVs to perform vision-based target track- ing, which entails in target tracking. I. INTRODUCTION Small unmanned aerial vehicles (UAVs) are relatively in- expensive
Chalkiadakis, Georgios
-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from onTowards Optimal Solar Tracking: A Dynamic Programming Approach Athanasios Aris Panagopoulos@ecs.soton.ac.uk Abstract The power output of photovoltaic systems (PVS) increases with the use of effective and efficient
An Automatic Design Optimization Tool and its Application to Computational Fluid Dynamics
David Abramson; Andrew Lewis; Tom Peachey; Clive Fletcher
2001-01-01
In this paper we describe the Nimrod\\/O design optimization tool, and its application in computational fluid dynamics. Nimrod\\/O facilitates the use of an arbitrary computational model to drive an automatic optimization process. This means that the user can parameterise an arbitrary problem, and then ask the tool to compute the parameter values that minimize or maximise a design objective function.
An automatic design optimization tool and its application to computational fluid dynamics
David Abramson; Andrew Lewis; Tom Peachey; Clive Fletcher
2001-01-01
In this paper we describe the Nimrod\\/O design optimization tool, and its application in computational fluid dynamics. Nimrod\\/O facilitates the use of an arbitrary computational model to drive an automatic optimization process. This means that the user can parameterise an arbitrary problem, and then ask the tool to compute the parameter values that minimize or maximise a design objective function.
OPTIMAL INTERVENTION IN THE FOREIGN EXCHANGE MARKET WHEN INTERVENTIONS AFFECT MARKET DYNAMICS
-variational inequalities, stopping times. 1. Introduction. In countries dependent on foreign trade and foreign capitalOPTIMAL INTERVENTION IN THE FOREIGN EXCHANGE MARKET WHEN INTERVENTIONS AFFECT MARKET DYNAMICS ALEC N. KERCHEVAL AND JUAN F. MORENO Abbreviated Title: Optimal intervention in foreign exchange Abstract
Optimal Central Place Hierarchies: A Dynamic Programming Approach (Preliminary)
Wen-Tai Hsu
Central place theory is a key building block of economic geography and it describes how a hierarchical city system with difierent layers of cities serving difierent sized market areas forms from a uniformly populated space. This paper provides a formal economic theory of optimal central place hierarchy, and it also compares the optimal hierarchy with the equilibrium hierarchy in Hsu
Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation
NASA Astrophysics Data System (ADS)
Krastev, Vladimir
2011-12-01
We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.
Optimal Campaign Strategies in Fractional-Order Smoking Dynamics
NASA Astrophysics Data System (ADS)
Zeb, Anwar; Zaman, Gul; Jung, Il Hyo; Khan, Madad
2014-06-01
This paper deals with the optimal control problem in the giving up smoking model of fractional order. For the eradication of smoking in a community, we introduce three control variables in the form of education campaign, anti-smoking gum, and anti-nicotive drugs/medicine in the proposed fractional order model. We discuss the necessary conditions for the optimality of a general fractional optimal control problem whose fractional derivative is described in the Caputo sense. In order to do this, we minimize the number of potential and occasional smokers and maximize the number of ex-smokers. We use Pontryagin's maximum principle to characterize the optimal levels of the three controls. The resulting optimality system is solved numerically by MATLAB.
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
Tailoring of composite links for optimal damped elasto-dynamic performance
NASA Technical Reports Server (NTRS)
Saravanos, D. A.; Chamis, C. C.
1989-01-01
A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.
To appear in ACM TOG 33(6). Dynamic Hair Capture using Spacetime Optimization
Grinspun, Eitan
work was done when Zexiang Xu was an intern at Microsoft. recognizable parts of a human body, hairTo appear in ACM TOG 33(6). Dynamic Hair Capture using Spacetime Optimization Zexiang Xu1 Hsiang, Beihang University Microsoft Research Columbia University Figure 1: Our dynamic hair capture system
On Using Complex Event Processing for Dynamic Demand Response Optimization in Microgrid
Hwang, Kai
On Using Complex Event Processing for Dynamic Demand Response Optimization in Microgrid Qunzhi Zhou Processing (CEP) patterns to model and detect dynamic situations in a campus microgrid to facilitate adaptive these patterns on realtime events in the USC Campus microgrid using our CEP framework. Index Terms
Optimal Dynamic Sleep Time Control in Wireless Sensor Networks Xu Ning and Christos G. Cassandras
Cassandras, Christos G.
Optimal Dynamic Sleep Time Control in Wireless Sensor Networks Xu Ning and Christos G. Cassandras the sleep interval between consecutive wake- ups of the receiver so that the expected total energy spent fixed sleep times to our dynamic control policy. I. INTRODUCTION A Wireless Sensor Network (WSN
Optimal solid shells for non-linear analyses of multilayer composites. II. Dynamics
Vu-Quoc, Loc
Optimal solid shells for non-linear analyses of multilayer composites. II. Dynamics L. Vu-Quoc *, X of large deformable multilayer shell structures using elements at extremely high aspect ratio. With the dynamics referred to a fixed inertial frame, the elements can be used to analyze multilayer shell
Assertion Guided Abstraction: A Cooperative Optimization for Dynamic Partial Order Reduction
Wang, Chao
Assertion Guided Abstraction: A Cooperative Optimization for Dynamic Partial Order Reduction Markus a new method, called assertion guided abstraction, which leverages both static and dynamic program to only those conflicting memory accesses that may cause assertion violations and/or deadlocks. Our
Anshu Saksena; I-jeng Wang
2008-01-01
In this paper we study the problem of dynamic optimization of ping schedule in an active sonar buoy network deployed to provide persistent surveillance of a littoral area through multistatic detection. The goal of ping scheduling is to dynamically determine when to ping and which ping source to engage in order to achieve the desirable detection performance. For applications where
The Performance of Runtime Data Cache Prefetching in a Dynamic Optimization System
Minnesota, University of
The Performance of Runtime Data Cache Prefetching in a Dynamic Optimization System Jiwei Lu controlled data cache prefetching is often ineffective due to the lack of runtime cache miss and miss address information. To overcome this limitation, we implement runtime data cache prefetching in the dynamic
NASA Astrophysics Data System (ADS)
Miyazaki, Takahiko; Akisawa, Atsushi; Kashiwagi, Takao
The cogeneration system provides electricity as well as heating and cooling, which consequently leads to a complexity of the design and operation of the system. It requires, therefore, the optimization of parameters such as the number of machines and the capacity of equipment. Generally, the problem can be expressed as a mixed integer nonlinear programming problem, and a lot of efforts would be required to solve it. In this paper, we present a different approach to the optimization of cogeneration systems, which facilitates to find a quasi-optimum solution. The particle swarm optimization combined with a simulation of the system is applied to the minimization of the primary energy consumption and of the system cost. The results present the optimum system constitutions for medium- and large-sized buildings. The result of the system cost minimization under a constraint of the energy saving rate is also discussed.
A multilevel optimization of large-scale dynamic systems
NASA Technical Reports Server (NTRS)
Siljak, D. D.; Sundareshan, M. K.
1976-01-01
A multilevel feedback control scheme is proposed for optimization of large-scale systems composed of a number of (not necessarily weakly coupled) subsystems. Local controllers are used to optimize each subsystem, ignoring the interconnections. Then, a global controller may be applied to minimize the effect of interconnections and improve the performance of the overall system. At the cost of suboptimal performance, this optimization strategy ensures invariance of suboptimality and stability of the systems under structural perturbations whereby subsystems are disconnected and again connected during operation.
Parameter optimization method for the water quality dynamic model based on data-driven theory.
Liang, Shuxiu; Han, Songlin; Sun, Zhaochen
2015-09-15
Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB). PMID:26277602
NASA Astrophysics Data System (ADS)
Emmelmann, Claus; Kirchhoff, Marc; Beckmann, Frank
Lightweight design is an important issue within the product development process, especially if components are subjected to dynamical movements. This paper is a systematical approach for the development of weight optimized components for laser remote scanners using topology optimization. A topology optimization of a mirror mount is demonstrated, starting with the determination of mechanical boundary conditions of a complex laser remote scanner. Based on the optimization results, the process of designing a component suitable for production is shown. Finally, the capability of this procedure for different components for various branches is elucidated.
Adaptive Optimal Feedback Control with Learned Internal Dynamics Models
Mitrovic, Djordje; Klanke, Stefan; Vijayakumar, Sethu
2010-01-01
Optimal Feedback Control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the Iterative Linear Quadratic ...
A Probability Distribution Estimation Based Method for Dynamic Optimization
Huang, Yinlun
consumption under various process/product constraints. Optimization results demonstrate superior- ities search utilizes three classical rules, i.e., the state transition rule, the local pheromone updating rule
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.
1995-01-01
This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.
1994-01-01
This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.
Ghosh, Arka P.
Optimal buffer size and dynamic rate control for a queueing network with impatient customers abandonment in heavy traffic. The controller can choose a buffer size for the queuing network and also can an asymptotically optimal control policy, i.e. an optimal buffer size and an optimal service rate for the queueing
Haihong Li; Zhiyong Yang; Tian Huang
2009-01-01
Dynamic modeling and analysis of a 2-DOF translational parallel robot with flexible links for high-speed pick-and-place operation\\u000a is presented in this paper. Optimization is implemented with the goal to improve the dynamic accuracy of the end-effector\\u000a at high speed. The governing equations of flexible links within the robot are formulated in the floating reference frame using\\u000a Euler–Lagrange method, leading to
Approximate dynamic programming recurrence relations for a hybrid optimal control problem
NASA Astrophysics Data System (ADS)
Lu, W.; Ferrari, S.; Fierro, R.; Wettergren, T. A.
2012-06-01
This paper presents a hybrid approximate dynamic programming (ADP) method for a hybrid dynamic system (HDS) optimal control problem, that occurs in many complex unmanned systems which are implemented via a hybrid architecture, regarding robot modes or the complex environment. The HDS considered in this paper is characterized by a well-known three-layer hybrid framework, which includes a discrete event controller layer, a discrete-continuous interface layer, and a continuous state layer. The hybrid optimal control problem (HOCP) is to nd the optimal discrete event decisions and the optimal continuous controls subject to a deterministic minimization of a scalar function regarding the system state and control over time. Due to the uncertainty of environment and complexity of the HOCP, the cost-to-go cannot be evaluated before the HDS explores the entire system state space; as a result, the optimal control, neither continuous nor discrete, is not available ahead of time. Therefore, ADP is adopted to learn the optimal control while the HDS is exploring the environment, because of the online advantage of ADP method. Furthermore, ADP can break the curses of dimensionality which other optimizing methods, such as dynamic programming (DP) and Markov decision process (MDP), are facing due to the high dimensions of HOCP.
A Dynamic Programming Approach to Determining Optimal Forest Wildfire
, line building rates, and other significant fire fighting traits. Because these considerations were construction productivity, response times, resource complementaries, size of fire upon discovery, rate of fire spread, fire damage, mop-up cost, and fire benefits. The dispatch optimization model has several
Optimal Routing-Conscious Dynamic Placement for Reconfigurable Devices
Fekete, SÃ¡ndor P.
Bobda1 , SÂ´andor P. Fekete2 , JÂ¨urgen Teich1 , and Jan C. van der Veen2 1 Department of Computer Science-conscious placement (which minimizes the total weighted Manhattan distance between the new module and existing demand sweep technique, optimal running time, lower bounds. 1 Introduction One of the cutting-edge aspects
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M
2010-01-01
Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. PMID:21969994
Performance evaluation of the inverse dynamics method for optimal spacecraft reorientation
NASA Astrophysics Data System (ADS)
Ventura, Jacopo; Romano, Marcello; Walter, Ulrich
2015-05-01
This paper investigates the application of the inverse dynamics in the virtual domain method to Euler angles, quaternions, and modified Rodrigues parameters for rapid optimal attitude trajectory generation for spacecraft reorientation maneuvers. The impact of the virtual domain and attitude representation is numerically investigated for both minimum time and minimum energy problems. Owing to the nature of the inverse dynamics method, it yields sub-optimal solutions for minimum time problems. Furthermore, the virtual domain improves the optimality of the solution, but at the cost of more computational time. The attitude representation also affects solution quality and computational speed. For minimum energy problems, the optimal solution can be obtained without the virtual domain with any considered attitude representation.
Topological optimization of mechanical amplifiers for piezoelectric actuators under dynamic motion
NASA Astrophysics Data System (ADS)
Du, Hejun; Keong Lau, Gih; King Lim, Mong; Qui, Jinhao
2000-12-01
Topological optimization is used to systematically design mechanical amplifiers that magnify the limited actuation stroke of a piezoelectric actuator. The design problem is posed as a material distribution problem using a variable thickness method. Two design goals are formulated for the design of the mechanical amplifier. They are the maximum dynamic stroke and the maximum dynamic magnification factor. The optimization problems are then solved using a method of moving asymptotes. The design domain is modelled as a plane-stress solid, and is actuated by harmonic excitation without the inclusion of damping. To model the actuator and workpiece, their stiffness is included and idealized as rod elements in the finite-element analysis. To show the capability of the design methodology, an elliptic amplifier and a magnification mechanism, used in a dot-matrix printer head, are reinvented using topological optimization. The dynamic effects on the optimum topology are also studied using different excitation frequencies.
Discrete Adjoint-Based Design Optimization of Unsteady Turbulent Flows on Dynamic Unstructured Grids
NASA Technical Reports Server (NTRS)
Nielsen, Eric J.; Diskin, Boris; Yamaleev, Nail K.
2009-01-01
An adjoint-based methodology for design optimization of unsteady turbulent flows on dynamic unstructured grids is described. The implementation relies on an existing unsteady three-dimensional unstructured grid solver capable of dynamic mesh simulations and discrete adjoint capabilities previously developed for steady flows. The discrete equations for the primal and adjoint systems are presented for the backward-difference family of time-integration schemes on both static and dynamic grids. The consistency of sensitivity derivatives is established via comparisons with complex-variable computations. The current work is believed to be the first verified implementation of an adjoint-based optimization methodology for the true time-dependent formulation of the Navier-Stokes equations in a practical computational code. Large-scale shape optimizations are demonstrated for turbulent flows over a tiltrotor geometry and a simulated aeroelastic motion of a fighter jet.
Liu, Xinli; Wang, Xicheng; Jiang, Huangliang
2008-04-24
In this paper, a steered molecular dynamics method with pulling direction optimization is proposed to dissociate ligand molecule from receptor. A multi-population genetic algorithm based on the information entropy is developed to search the optimal pulling direction. By imposing an optimization phase in the conventional steered molecular dynamics simulation, a better substrate-exit channel for the buried active site can be found. The novel simulation method has been used to dissociate the substrate-bound complex structure of cytochrome P450 3A4-metyrapone. The results show that the new pathway obtained by the proposed method has advantages such as lower energy barrier, less dissociation time and shorter motion trajectory than that by the conventional steered molecular dynamics. PMID:18031823
NASA Astrophysics Data System (ADS)
Butler, Thomas; Goldenfeld, Nigel; Mathew, Damien; Luthey-Schulten, Zaida
2009-06-01
A molecular dynamics calculation of the amino acid polar requirement is used to score the canonical genetic code. Monte Carlo simulation shows that this computational polar requirement has been optimized by the canonical genetic code, an order of magnitude more than any previously known measure, effectively ruling out a vertical evolution dynamics. The sensitivity of the optimization to the precise metric used in code scoring is consistent with code evolution having proceeded through the communal dynamics of statistical proteins using horizontal gene transfer, as recently proposed. The extreme optimization of the genetic code therefore strongly supports the idea that the genetic code evolved from a communal state of life prior to the last universal common ancestor.
NASA Astrophysics Data System (ADS)
Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng
2015-10-01
Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.
Ben Niu; Yunlong Zhu; Xiaoxian He
2005-01-01
\\u000a A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control\\u000a of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several\\u000a slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity\\u000a of particles, while the particles in the
Use of Ultrafast Molecular Dynamics and Optimal Control for Identifying Biomolecules
Jean-Pierre Wolf
2008-01-01
With F.COURVOISIER,L.GUYON,V.BOUTOU, and M.ROTH,J. ROSLUND, H. RABITZ, Princeton University. The identification and discrimination of molecules that exhibit almost identical structures and spectra using fluorescence spectroscopy is considered quite difficult. In order to evaluate the capability of optimal control for discriminating between the optical emissions of nearly identical molecules, we developed a new approach called ``optimal dynamic discrimination (ODD). A proof
Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades
NASA Technical Reports Server (NTRS)
Peters, David A.
1991-01-01
Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.
Dynamic modeling and optimization for space logistics using time-expanded networks
NASA Astrophysics Data System (ADS)
Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert
2014-12-01
This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.
INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P
2012-10-01
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1990-01-01
A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.
Dynamical symmetry breaking with optimal control: Reducing the number of pieces
NASA Astrophysics Data System (ADS)
Power, Matthew J. M.; De Chiara, Gabriele
2013-12-01
We analyze the production of defects during the dynamical crossing of a mean-field phase transition with a real order parameter. When the parameter that brings the system across the critical point changes in time according to a power-law schedule, we recover the predictions dictated by the well-known Kibble-Zurek theory. For a fixed duration of the evolution, we show that the average number of defects can be drastically reduced for a very large but finite system, by optimizing the time dependence of the driving using optimal control techniques. Furthermore, the optimized protocol is robust against small fluctuations.
Time limited optimal dynamics beyond the Quantum Speed Limit
Miroslav Gajdacz; Kunal K. Das; Jan Arlt; Jacob F. Sherson; Tomáš Opatrný
2015-05-22
The quantum speed limit sets the minimum time required to transfer a quantum system completely into a given target state. At shorter times the higher operation speed has to be paid with a loss of fidelity. Here we quantify the trade-off between the fidelity and the duration in a system driven by a time-varying control. The problem is addressed in the framework of Hilbert space geometry offering an intuitive interpretation of optimal control algorithms. This approach is applied to non-uniform time variations which leads to a necessary criterion for control optimality applicable as a measure of algorithm convergence. The time fidelity trade-off expressed in terms of the direct Hilbert velocity provides a robust prediction of the quantum speed limit and allows to adapt the control optimization such that it yields a predefined fidelity. The results are verified numerically in a multilevel system with a constrained Hamiltonian, and a classification scheme for the control sequences is proposed based on their optimizability.
A new method of optimal design for a two-dimensional diffuser by using dynamic programming
NASA Technical Reports Server (NTRS)
Gu, Chuangang; Zhang, Moujin; Chen, XI; Miao, Yongmiao
1991-01-01
A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.
Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes
the vast amount of research on anticipatory gaze behaviour in natural situations, such as action execution by introducing camera motion, jump cuts, and film-editing. Keywords Eye movements Ã Anticipatory gaze behaviour Ã collected and examined on a wide variety of dynamic realistic stimulus types (i.e. video cat- egories). Gaze
Logic optimization by output phase assignment in dynamic logic synthesis
Ruchir Puri; Andrew Bjorksten; Thomas E. Rosser
1996-01-01
Domino logic is one of the most popular dynamic circuit configurations for implementing high- performance logic designs. Since domino logic is inherently non-inverting, it presents a fundamental constraint of implementing logic functions without any intermediate inversions. Removal of intermediate inverters requires logic duplication for generating both the negative and positive signal phases, which results in significant area overhead. This area
Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization
Brad L. Miller; Michael J. Shaw
1996-01-01
Genetic algorithms utilize populations of individualhypotheses that converge over time to a singleoptimum, even within a multimodal domain. This paperexamines methods that enable genetic algorithms to identifymultiple optima within multimodal domains by maintainingpopulation members within the niches defined bythe multiple optima. A new mechanism, Dynamic NicheSharing, is developed that is able to efficiently identifyand search multiple niches (peaks) in a
Seizure warning algorithm based on optimization and nonlinear dynamics
Panos M. Pardalos; Wanpracha Art Chaovalitwongse; Leonidas D. Iasemidis; J. Chris Sackellares; Deng-shan Shiau; Paul R. Carney; Oleg A. Prokopyev; Vitaliy A. Yatsenko
2004-01-01
There is growing evidence that temporal lobe seizures are preceded by a preictal transition, characterized by a gradual dynamical change from asymptomatic interictal state to seizure. We herein report the first prospective analysis of the online automated algorithm for detecting the preictal transition in ongoing EEG signals. Such, the algorithm constitutes a seizure warning system. The algorithm estimates STL max,
Dynamic Modeling and Recipe Optimization of Polyether Polyol Processes
Grossmann, Ignacio E.
Epoxides (ethylene oxide (EO), propylene oxide (PO)) OO OO Molecules containing active hydrogen atoms of the obtained model A large-scale differential-algebraic equation (DAE) system Synergistic fast and slow dynamic modes caused by exchange reactions Stiff differential equations A nullspace projection method
Dynamic Modeling and Recipe Optimization of Polyether Polyol Processes
Grossmann, Ignacio E.
OO Molecules containing active hydrogen atoms (alcohols, amines) OH N H H A basic catalyst (KOH) Yisu OO Molecules containing active hydrogen atoms (alcohols, amines) OH N H H A basic catalyst (KOH) N2-scale differential-algebraic equation (DAE) system Synergistic fast and slow dynamic modes Caused by fast exchange
Dynamic Optimal Random Access for Vehicle-to-Roadside Communications
Huang, Jianwei
opportunity. In this paper, we study random access in vehicle-to-roadside (V2R) communications in a dynamic of communication patterns, including vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) com- munications. V2R communications involve data transmissions between vehicular nodes and roadside APs, and V2V commu- nications
Optimal Design and Operation of Permanent Irrigation Systems
NASA Astrophysics Data System (ADS)
Oron, Gideon; Walker, Wynn R.
1981-01-01
Solid-set pressurized irrigation system design and operation are studied with optimization techniques to determine the minimum cost distribution system. The principle of the analysis is to divide the irrigation system into subunits in such a manner that the trade-offs among energy, piping, and equipment costs are selected at the minimum cost point. The optimization procedure involves a nonlinear, mixed integer approach capable of achieving a variety of optimal solutions leading to significant conclusions with regard to the design and operation of the system. Factors investigated include field geometry, the effect of the pressure head, consumptive use rates, a smaller flow rate in the pipe system, and outlet (sprinkler or emitter) discharge.
Optimal purchasing of raw materials: A data-driven approach
Muteki, K.; MacGregor, J.F. [McMaster University, Hamilton, ON (Canada). Dept. of Chemical Engineering
2008-06-15
An approach to the optimal purchasing of raw materials that will achieve a desired product quality at a minimum cost is presented. A PLS (Partial Least Squares) approach to formulation modeling is used to combine databases on raw material properties and on past process operations and to relate these to final product quality. These PLS latent variable models are then used in a sequential quadratic programming (SQP) or mixed integer nonlinear programming (MINLP) optimization to select those raw-materials, among all those available on the market, the ratios in which to combine them and the process conditions under which they should be processed. The approach is illustrated for the optimal purchasing of metallurgical coals for coke making in the steel industry.
2012-01-01
Background Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations. This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Results Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. Conclusions In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems. PMID:22748139
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
HIV dynamics: Modeling, data analysis, and optimal treatment protocols
NASA Astrophysics Data System (ADS)
Adams, B. M.; Banks, H. T.; Davidian, M.; Kwon, Hee-Dae; Tran, H. T.; Wynne, S. N.; Rosenberg, E. S.
2005-12-01
We present an overview of some concepts and methodologies we believe useful in modeling HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical and statistical ideas relevant to Structured Treatment Interruptions (STI). Among these are model development and validation procedures including parameter estimation, data reduction and representation, and optimal control relative to STI. Results from initial attempts in each of these areas by an interdisciplinary team of applied mathematicians, statisticians and clinicians are presented.
A stable elemental decomposition for dynamic process optimization
NASA Astrophysics Data System (ADS)
Cervantes, Arturo M.; Biegler, Lorenz T.
2000-08-01
In Cervantes and Biegler (A.I.Ch.E.J. 44 (1998) 1038), we presented a simultaneous nonlinear programming problem (NLP) formulation for the solution of DAE optimization problems. Here, by applying collocation on finite elements, the DAE system is transformed into a nonlinear system. The resulting optimization problem, in which the element placement is fixed, is solved using a reduced space successive quadratic programming (rSQP) algorithm. The space is partitioned into range and null spaces. This partitioning is performed by choosing a pivot sequence for an LU factorization with partial pivoting which allows us to detect unstable modes in the DAE system. The system is stabilized without imposing new boundary conditions. The decomposition of the range space can be performed in a single step by exploiting the overall sparsity of the collocation matrix but not its almost block diagonal structure. In order to solve larger problems a new decomposition approach and a new method for constructing the quadratic programming (QP) subproblem are presented in this work. The decomposition of the collocation matrix is now performed element by element, thus reducing the storage requirements and the computational effort. Under this scheme, the unstable modes are considered in each element and a range-space move is constructed sequentially based on decomposition in each element. This new decomposition improves the efficiency of our previous approach and at the same time preserves its stability. The performance of the algorithm is tested on several examples. Finally, some future directions for research are discussed.
Stochastic Dynamic Programming Models for Reservoir Operation Optimization
NASA Astrophysics Data System (ADS)
Stedinger, Jery R.; Sule, Bola F.; Loucks, Daniel P.
1984-11-01
Most applications of stochastic dynamic programming have derived stationary policies which use the previous period's inflow as a hydrologic state variable. This paper develops a stochastic dynamic programming model which employs the best forecast of the current period's inflow to define a reservoir release policy and to calculate the expected benefits from future operations. Use of the best inflow forecast as a hydrologic state variable, instead of the preceding period's inflow, resulted in substantial improvements in simulated reservoir operations with derived stationary reservoir operating policies. While these results are for a dam at Aswan in the Nile River Basin, operators of other reservoir systems also have available to them information other than the preceding period's inflow which can be used to develop improved inflow forecasts.
Dynamic programming algorithm optimization for spoken word recognition
HIROAKI SAKOE; SEIBI CHIBA
1978-01-01
This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm
Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization
Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar
2014-01-01
The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness. PMID:25794375
Optimal control landscape for the generation of unitary transformations with constrained dynamics
Hsieh, Michael [Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90089 (United States); Department of Chemistry, University of Southern California, Los Angeles, California 90089 (United States); Wu, Rebing [Department of Automation, Tsinghua University, Beijing, 100084 (China); Rabitz, Herschel [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States); Lidar, Daniel [Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90089 (United States); Department of Chemistry, University of Southern California, Los Angeles, California 90089 (United States); Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089 (United States); Department of Physics, University of Southern California, Los Angeles, California 90089 (United States)
2010-06-15
The reliable and precise generation of quantum unitary transformations is essential for the realization of a number of fundamental objectives, such as quantum control and quantum information processing. Prior work has explored the optimal control problem of generating such unitary transformations as a surface-optimization problem over the quantum control landscape, defined as a metric for realizing a desired unitary transformation as a function of the control variables. It was found that under the assumption of nondissipative and controllable dynamics, the landscape topology is trap free, which implies that any reasonable optimization heuristic should be able to identify globally optimal solutions. The present work is a control landscape analysis, which incorporates specific constraints in the Hamiltonian that correspond to certain dynamical symmetries in the underlying physical system. It is found that the presence of such symmetries does not destroy the trap-free topology. These findings expand the class of quantum dynamical systems on which control problems are intrinsically amenable to a solution by optimal control.
Improving the dynamic characteristics of body-in-white structure using structural optimization.
Yahaya Rashid, Aizzat S; Ramli, Rahizar; Mohamed Haris, Sallehuddin; Alias, Anuar
2014-01-01
The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312
Synthesizing optimal waste blends
Narayan, V.; Diwekar, W.M. [Carnegie Mellon Univ., Pittsburgh, PA (United States)] [Carnegie Mellon Univ., Pittsburgh, PA (United States); Hoza, M. [Pacific Northwest Lab., Richland, WA (United States)] [Pacific Northwest Lab., Richland, WA (United States)
1996-10-01
Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. Process and storage economics show that minimizing the total number of glass logs produced is the key to keeping cost as low as possible. The amount of glass produced can be reduced by blending of the wastes. The optimal way to combine the tanks to minimize the vole of glass can be determined from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make this problem highly nonconvex where many algorithms get trapped in local minima. In this paper the authors examine the use of different combinatorial optimization approaches to solve this problem. A two-stage approach using a combination of simulated annealing and nonlinear programming (NLP) is developed. The results of different methods such as the heuristics approach based on human knowledge and judgment, the mixed integer nonlinear programming (MINLP) approach with GAMS, and branch and bound with lower bound derived from the structure of the given blending problem are compared with this coupled simulated annealing and NLP approach.
Multi-host transmission dynamics of schistosomiasis and its optimal control.
Ding, Chunxiao; Qiu, Zhipeng; Zhu, Huaiping
2015-10-01
In this paper we formulate a dynamical model to study the transmission dynamics of schistosomiasis in humans and snails. We also incorporate bovines in the model to study their impact on transmission and controlling the spread of Schistosoma japonicum in humans in China. The dynamics of the model is rigorously analyzed by using the theory of dynamical systems. The theoretical results show that the disease free equilibrium is globally asymptotically stable if R0 < 1, and if R0 > 1 the system has only one positive equilibrium. The local stability of the unique positive equilibrium is investigated and sufficient conditions are also provided for the global stability of the positive equilibrium. The optimal control theory are further applied to the model to study the corresponding optimal control problem. Both analytical and numerical results suggest that: (a) the infected bovines play an important role in the spread of schistosomiasis among humans, and killing the infected bovines will be useful to prevent transmission of schistosomiasis among humans; (b) optimal control strategy performs better than the constant controls in reducing the prevalence of the infected human and the cost for implementing optimal control is much less than that for constant controls; and PMID:26280190
William F. Eddy; Audris Mockus
1995-01-01
We consider visualization as a decision optimization tool in problems where the model and\\/or the objectives are not well defined. We investigate four specificproblems representing different degrees of determination. The first problem concerns a smooth dynamic representation of data collected at fixed locations. In the example we want to minimize the deviations from a desired temperature over space and time.
Content-based medical image retrieval using dynamically optimized regional features
Wei Xiong; Bo Qiu; Qi Tian; Changsheng Xu; Sim Heng Ong; Kelvin W. C. Foong
2005-01-01
This paper proposes a content-based medical image retrieval (CBMIR) framework using dynamically optimized features from multiple regions of medical images. These regional features, including structural and statistical properties of color, texture and geometry, are extracted from multiple dominant regions segmented by applying Gaussian mixture modeling (GMM) and the expectation maximization (EM) algorithm to medical images. Over them, principal component analysis
Flow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process
Grujicic, Mica
such as high-velocity oxy-fuel, detonation gun, plasma spray and arc spray are widely used to apply protective of deposited material. Cold spray as a coating technology was initially developed in the mid-1980sFlow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process M Grujicic1*, W
Stentz, Tony
Abstract Planning for multiple mobile robots in dynamic environ ments involves determining the optimal path each robot should follow to accomplish the goals of the mission, given the current knowledge reassign robots to goals in order to continually minimize the time to complete the mission. In this paper
Optimal transmission policies for two-user multiple access broadcast using dynamic team theory
Mahajan, Aditya
, and Teneketzis, 2008 for a dynamic team problem to be tractable. Using the idea of a virtual coordinator the optimal window protocol proposed by Hluchyj and Gallager, 1981. Thus, this paper presents the an example] investigated the two-user MABC with symmetric arrivals by restricting attention to window protocols. Thus
Object-oriented Dynamics Modeling for Legged Robot Trajectory Optimization and Control
Stryk, Oskar von
requirements by legged robot applications. This leads to a uniform, modular, and flexible code generation whileObject-oriented Dynamics Modeling for Legged Robot Trajectory Optimization and Control Robert problems of bipedal and quadrupedal robots are investigated as applications. It is shown how a high
Optimal intervention in the foreign exchange market when interventions affect market dynamics
Aluffi, Paolo
, foreign exchange intervention. 1 Introduction In countries dependent on foreign trade and foreign capitalOptimal intervention in the foreign exchange market when interventions affect market dynamics Alec) forms: adjustment of domestic interest rate levels, which influences the attractiveness of foreign
A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION
Kuhl, Michael E.
A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION Michael E INTRODUCTION Project management is a tool that is used by many compa- nies to help improve performance and competitiveness. Projects and their execution, in general, require resources. Project management, which
Optimal Passive Dynamics for Torque/Force Control Kevin Kemper, Devin Koepl and Jonathan Hurst
Hurst, Jonathan
Optimal Passive Dynamics for Torque/Force Control Kevin Kemper, Devin Koepl and Jonathan Hurst-- For robotic manipulation tasks in uncertain envi- ronments, good force control can provide significant benefits. The design of force-controlled actuators typically revolves around developing the best possible
Ant Colony Optimization Algorithms with Local Search for the Dynamic Vehicle Routing Problem
Andrew Runka
Abstract This report demonstrates the use of eective,local search to im- prove the performance of simple Ant Colony Optimization (ACO) algorithms as applied to an extension of the Vehicle Routing Problem (VRP) known as the Dynamic Vehicle Routing Problem (DVRP). The static VRP presents all orders a priori, however the DVRP requires scheduling to begin without a complete knowledge of
Dynamic Optimization of Lean Burn Engine Aftertreatment Jun-Mo Kang
Grizzle, Jessy W.
Dynamic Optimization of Lean Burn Engine Aftertreatment Jun-Mo Kang Ph.D. University of Michigan Programming to make model-based design decisions for a lean burn, direct injection spark ignition engine, in combination with a three way catalyst and an additional three- way catalyst, often referred to as a lean NOx
Bipedal Robotic Running with Partial Hybrid Zero Dynamics and Human-Inspired Optimization
Ames, Aaron
more mobility and energy efficiency compared to walking. In the field of humanoid bipedal locomotionBipedal Robotic Running with Partial Hybrid Zero Dynamics and Human-Inspired Optimization Huihua for achieving stable "human-like" running in simulation by using human-inspired control. Data from human running
Dynamic online optimization of a house heating system in a fluctuating energy price
Skogestad, Sigurd
Dynamic online optimization of a house heating system in a fluctuating energy price scenario in this problem is the time- varying nature of the main disturbances, which are the energy price and outdoor energy price. 1. INTRODUCTION Recently, great attention has been given to renewable gen- eration sources
Optimal drag coefficient histories which extremize dynamic pressure for a ballistic reentry vehicle
W. E. Jr
1978-01-01
The problem of determining the drag coefficient history for a reentry vehicle which produces minimum or maximum dynamic pressure for specified end conditions is formulated. The optimal drag coefficient history is required to be inside specified bounds. The problem is solved when the final time is fixed and also when the final altitude is fixed. Results show that jumps in
Optimal unsteady convection over a duty cycle for arbitrary unsteady flow under dynamic thermal load
Bahrami, Majid
- cles (EV), and emerging fuel cell vehicles (FCV) [1,2] and green power systems (wind, solar, tidal) [3 power electronics and electric machines (APEEM) inside hybrid electric, electric, and fuel cell vehicles as well as renewable energies (wind, solar, tidal). Optimal design criteria for such dynamic heat
Carlson, Jean
2005-01-01
Journal of Theoretical Biology 236 (2005) 438447 Evolutionary dynamics and highly optimized of processes which are commonly discussed in biological and ecological case studies. These include the effects reserved. Keywords: Complexity; Evolution; Robustness 1. Introduction Biology and ecology are rich
Quantum optimal control theory and dynamic coupling in the spin-boson model
Jirari, H.; Poetz, W.
2006-08-15
A Markovian master equation describing the evolution of open quantum systems in the presence of a time-dependent external field is derived within the Bloch-Redfield formalism. It leads to a system-bath interaction which depends on the control field. Optimal control theory is used to select control fields which allow accelerated or decelerated system relaxation, or suppression of relaxation (dissipation) altogether, depending on the dynamics we impose on the quantum system. The control-dissipation correlation and the nonperturbative treatment of the control field are essential for reaching this goal. The optimal control problem is formulated within Pontryagin's minimum principle and the resulting optimal differential system is solved numerically. As an application, we study the dynamics of a spin-boson model in the strong coupling regime under the influence of an external control field. We show how trapping the system in unstable quantum states and transfer of population can be achieved by optimized control of the dissipative quantum system. We also used optimal control theory to find the driving field that generates the quantum Z gate. In several cases studied, we find that the selected optimal field which reduces the purity loss significantly is a multicomponent low-frequency field including higher harmonics, all of which lie below the phonon cutoff frequency. Finally, in the undriven case we present an analytic result for the Lamb shift at zero temperature.
Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.
Huang, Xuelin; Choi, Sangbum; Wang, Lu; Thall, Peter F
2015-11-20
In medical therapies involving multiple stages, a physician's choice of a subject's treatment at each stage depends on the subject's history of previous treatments and outcomes. The sequence of decisions is known as a dynamic treatment regime or treatment policy. We consider dynamic treatment regimes in settings where each subject's final outcome can be defined as the sum of longitudinally observed values, each corresponding to a stage of the regime. Q-learning, which is a backward induction method, is used to first optimize the last stage treatment then sequentially optimize each previous stage treatment until the first stage treatment is optimized. During this process, model-based expectations of outcomes of late stages are used in the optimization of earlier stages. When the outcome models are misspecified, bias can accumulate from stage to stage and become severe, especially when the number of treatment stages is large. We demonstrate that a modification of standard Q-learning can help reduce the accumulated bias. We provide a computational algorithm, estimators, and closed-form variance formulas. Simulation studies show that the modified Q-learning method has a higher probability of identifying the optimal treatment regime even in settings with misspecified models for outcomes. It is applied to identify optimal treatment regimes in a study for advanced prostate cancer and to estimate and compare the final mean rewards of all the possible discrete two-stage treatment sequences. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26095711
Zhong, Xiangnan; He, Haibo; Zhang, Huaguang; Wang, Zhanshan
2014-12-01
In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic programming technique. We also develop detailed stability analysis of the control approach, including the convergence of the performance index function for nonlinear MJSs and the existence of the corresponding admissible control. Neural network techniques are used to approximate the proposed performance index function and the control law. To demonstrate the effectiveness of our approach, three simulation studies, one linear case, one nonlinear case, and one single link robot arm case, are used to validate the performance of the proposed optimal control method. PMID:25420238
Characterization of control noise effects in optimal quantum unitary dynamics
NASA Astrophysics Data System (ADS)
Hocker, David; Brif, Constantin; Grace, Matthew D.; Donovan, Ashley; Ho, Tak-San; Tibbetts, Katharine Moore; Wu, Rebing; Rabitz, Herschel
2014-12-01
This work develops measures for quantifying the effects of field noise upon targeted unitary transformations. Robustness to noise is assessed in the framework of the quantum control landscape, which is the mapping from the control to the unitary transformation performance measure (quantum gate fidelity). Within that framework, a geometric interpretation of stochastic noise effects naturally arises, where more robust optimal controls are associated with regions of small overlap between landscape curvature and the noise correlation function. Numerical simulations of this overlap in the context of quantum information processing reveal distinct noise spectral regimes that better support robust control solutions. This perspective shows the dual importance of both noise statistics and the control form for robustness, thereby opening up new avenues of investigation on how to mitigate noise effects in quantum systems.
Optimizing quantum correlation dynamics by weak measurement in dissipative environment
NASA Astrophysics Data System (ADS)
Du, Shao-Jiang; Xia, Yun-Jie; Duan, De-Yang; Zhang, Lu; Gao, Qiang
2015-04-01
We investigate the protection of quantum correlations of two qubits in independent vacuum reservoirs by means of weak measurements. It is found that the weak measurement can reduce the amount of quantum correlation for one type of initial state at the beginning in a non-Markovian environment and meanwhile it can reduce the occurrence time of entanglement sudden death (ESD) in the process of time evolution. In a Markovian environment, the quantum entanglements of the two kinds of initial states decay rapidly and the weak measurement can further weaken the quantum entanglement, therefore in this case the entanglement cannot be optimized in the evolution process. Project supported by the National Natural Science Foundation of China (Grant Nos. 61178012 and No.11147019).
Characterization of control noise effects in optimal quantum unitary dynamics
David Hocker; Constantin Brif; Matthew D. Grace; Ashley Donovan; Tak-San Ho; Katharine Moore Tibbetts; Rebing Wu; Herschel Rabitz
2014-11-13
This work develops measures for quantifying the effects of field noise upon targeted unitary transformations. Robustness to noise is assessed in the framework of the quantum control landscape, which is the mapping from the control to the unitary transformation performance measure (quantum gate fidelity). Within that framework, a new geometric interpretation of stochastic noise effects naturally arises, where more robust optimal controls are associated with regions of small overlap between landscape curvature and the noise correlation function. Numerical simulations of this overlap in the context of quantum information processing reveal distinct noise spectral regimes that better support robust control solutions. This perspective shows the dual importance of both noise statistics and the control form for robustness, thereby opening up new avenues of investigation on how to mitigate noise effects in quantum systems.
Yitao Zhu; Daniel Dopico; Corina Sandu; Adrian Sandu
2014-10-30
Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization. A very important first step towards design optimization is sensitivity analysis of multibody system dynamics. Dynamic sensitivities are often calculated by means of finite differences. Depending of the number of parameters involved, this procedure can be computationally expensive. Moreover, in many cases, the results suffer from low accuracy when real perturbations are used. The main contribution to the state-of-the-art brought by this study is the development of the adjoint sensitivity approach of multibody systems in the context of the penalty formulation. The theory developed is demonstrated on one academic case study, a five-bar mechanism, and on one real-life system, a 14-DOF vehicle model. The five-bar mechanism is used to illustrate the sensitivity approach derived in this paper. The full vehicle model is used to demonstrate the capability of the new approach developed to perform sensitivity analysis and gradient-based optimization for large and complex multibody systems with respect to multiple design parameters.
Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties
NASA Technical Reports Server (NTRS)
He, Cheng-Jian; Peters, David A.
1990-01-01
Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.
NASA Astrophysics Data System (ADS)
Wei, Qing-Lai; Liu, De-Rong; Xu, Yan-Cai
2015-03-01
A policy iteration algorithm of adaptive dynamic programming (ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then, the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks, the developed optimal tracking control scheme for chaotic systems is verified by a simulation. Project supported by the National Natural Science Foundation of China (Grant Nos. 61034002, 61233001, 61273140, 61304086, and 61374105) and the Beijing Natural Science Foundation, China (Grant No. 4132078).
NASA Technical Reports Server (NTRS)
Zang, Thomas A.; Green, Lawrence L.
1999-01-01
A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.
Designing dynamical properties of brain-machine interfaces to optimize task-specific performance.
Gowda, Suraj; Orsborn, Amy L; Overduin, Simon A; Moorman, Helene G; Carmena, Jose M
2014-09-01
Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance. However, there have been few empirical studies exploring how various dynamical effects of closed-loop BMIs ultimately influence performance. In this work, we utilize experimental data from two macaque monkeys operating a closed-loop BMI to reach to 2-D targets and show that certain dynamical properties correlate with performance loss. We also show that other dynamical properties represent tradeoffs between naturally competing objectives, such as speed versus accuracy. These findings highlight the importance of fine-tuning the dynamical properties of closed-loop BMIs to optimize task-specific performance. PMID:24760941
NASA Astrophysics Data System (ADS)
Souvatzis, Petros; Niklasson, Anders M. N.
2013-12-01
We present an efficient general approach to first principles molecular dynamics simulations based on extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The reduction of the optimization requirement reduces the computational cost to a minimum, but without causing any significant loss of accuracy or long-term energy drift. The optimization-free first principles molecular dynamics requires only one single diagonalization per time step, but is still able to provide trajectories at the same level of accuracy as "exact," fully converged, Born-Oppenheimer molecular dynamics simulations. The optimization-free limit of extended Lagrangian Born-Oppenheimer molecular dynamics therefore represents an ideal starting point for robust and efficient first principles quantum mechanical molecular dynamics simulations.
Souvatzis, Petros, E-mail: petros.souvatsiz@fysik.uu.se [Department of Physics and Astronomy, Division of Materials Theory, Uppsala University, Box 516, SE-75120, Uppsala (Sweden)] [Department of Physics and Astronomy, Division of Materials Theory, Uppsala University, Box 516, SE-75120, Uppsala (Sweden); Niklasson, Anders M. N., E-mail: amn@lanl.gov [Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)] [Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)
2013-12-07
We present an efficient general approach to first principles molecular dynamics simulations based on extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The reduction of the optimization requirement reduces the computational cost to a minimum, but without causing any significant loss of accuracy or long-term energy drift. The optimization-free first principles molecular dynamics requires only one single diagonalization per time step, but is still able to provide trajectories at the same level of accuracy as “exact,” fully converged, Born-Oppenheimer molecular dynamics simulations. The optimization-free limit of extended Lagrangian Born-Oppenheimer molecular dynamics therefore represents an ideal starting point for robust and efficient first principles quantum mechanical molecular dynamics simulations.
Mdluli, Thembi; Buzzard, Gregery T.; Rundell, Ann E.
2015-01-01
This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements. PMID:26379275
Computational fluid dynamics based bulbous bow optimization using a genetic algorithm
NASA Astrophysics Data System (ADS)
Mahmood, Shahid; Huang, Debo
2012-09-01
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of "total resistance". Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Collision-free nonuniform dynamics within continuous optimal velocity models
NASA Astrophysics Data System (ADS)
Tordeux, Antoine; Seyfried, Armin
2014-10-01
Optimal velocity (OV) car-following models give with few parameters stable stop-and -go waves propagating like in empirical data. Unfortunately, classical OV models locally oscillate with vehicles colliding and moving backward. In order to solve this problem, the models have to be completed with additional parameters. This leads to an increase of the complexity. In this paper, a new OV model with no additional parameters is defined. For any value of the inputs, the model is intrinsically asymmetric and collision-free. This is achieved by using a first-order ordinary model with two predecessors in interaction, instead of the usual inertial delayed first-order or second-order models coupled with the predecessor. The model has stable uniform solutions as well as various stable stop-and -go patterns with bimodal distribution of the speed. As observable in real data, the modal speed values in congested states are not restricted to the free flow speed and zero. They depend on the form of the OV function. Properties of linear, concave, convex, or sigmoid speed functions are explored with no limitation due to collisions.
Optimal mutation rates in dynamic environments: The eigen model
NASA Astrophysics Data System (ADS)
Ancliff, Mark; Park, Jeong-Man
2011-03-01
We consider the Eigen quasispecies model with a dynamic environment. For an environment with sharp-peak fitness in which the most-fit sequence moves by k spin-flips each period T we find an asymptotic stationary state in which the quasispecies population changes regularly according to the regular environmental change. From this stationary state we estimate the maximum and the minimum mutation rates for a quasispecies to survive under the changing environment and calculate the optimum mutation rate that maximizes the population growth. Interestingly we find that the optimum mutation rate in the Eigen model is lower than that in the Crow-Kimura model, and at their optimum mutation rates the corresponding mean fitness in the Eigen model is lower than that in the Crow-Kimura model, suggesting that the mutation process which occurs in parallel to the replication process as in the Crow-Kimura model gives an adaptive advantage under changing environment.
Computational Mixed Integer Nonlinear Programming Jeff "Obi-Wan" Linderoth
Willett, Rebecca
Application: Death Star Core Reload Maximize reactor efficiency after reload subject to diffusion PDE Linderoth (UW-Madison) MINLP Wars WIDDOW 5 / 70 #12;Introduction Applications Application: Death Star Core
An algorithmic framework for convex mixed integer nonlinear programs
Grossmann, Ignacio E.
of open-source software for problems in operations research. In particular, COIN-OR contains reusable forces to study algorithms for MINLPs and develop associated open-source software, leveraging components the first step in an ongoing and ambitious project within an open-source environment. COIN-OR is our chosen
A stochastic mixed integer programming approach to wildfire management systems
Lee, Won Ju
2009-06-02
: : : : : : : : : : : : : : : : : : : : : : : : : : 1 II LITERATURE REVIEW : : : : : : : : : : : : : : : : : : : : : : 4 A. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 4 B. Strategic Decision . . . . . . . . . . . . . . . . . . . . . . . 9 C. Tactical Decision... scenarios : : : : : : : : : : : : : : : : : : : : : : : : : : : 100 xii LIST OF FIGURES FIGURE Page 1 Cost + NVC[9] : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 2 Convergence of wfcp 7 6 5 : : : : : : : : : : : : : : : : : : : : : : : : 50 3...
Application of mixed-integer programming in chemical engineering
Pogiatzis, Thomas
2013-06-11
in Part II posi position of city i in decimal base Q set of vertices (cities) in Part I; heat duty in Part II Q0 heat duty for a clean heat exchanger qi Lagrange interpolation polynomial rc coke formation rate Rf thermal fouling resistance rf function... transfer coefficient U ? set of units U0 overall heat transfer coefficient for a clean heat exchanger ui continuous variable in sequential formulation of the travelling sales- man problem V set of vertices (cities) v continuous variable w function wij...
Applications and algorithms for mixed integer nonlinear programming
Linderoth, Jeffrey T.
of intercept from a discrete set of materials. The choice of material affects the thermal conductivity of electric power systems [2], the design of water distribution networks [3], operational reloading of nuclear MINLP tools to address their challenging scientific problems. 2. Applications Design of thermal
Mixed-Integer Nonlinear Programming Michael R. Bussieck Armin Pruessner
Neumaier, Arnold
(and even con- vex) nonlinear programs (NLP). Because subclasses MIP and NLP are among the class be a challenging and daring venture. Fortunately, the component structure of MIP and NLP within MINLP provides solutions of closely related NLP problems. For example, B&B starts out forming a pure continuous NLP problem
NASA Astrophysics Data System (ADS)
Koch, Caleb; Winfrey, Leigh
2014-10-01
Natural Gas is a major energy source in Europe, yet political instabilities have the potential to disrupt access and supply. Energy resilience is an increasingly essential construct and begins with transmission network design. This study proposes a new way of thinking about modelling natural gas flow. Rather than relying on classical economic models, this problem is cast into a time-dependent Hamiltonian dynamics discussion. Traditional Natural Gas constraints, including inelastic demand and maximum/minimum pipe flows, are portrayed as energy functions and built into the dynamics of each pipe flow. Doing so allows the constraints to be built into the dynamics of each pipeline. As time progresses in the model, natural gas flow rates find the minimum energy, thus the optimal gas flow rates. The most important result of this study is using dynamical principles to ensure the output of natural gas at demand nodes remains constant, which is important for country to country natural gas transmission. Another important step in this study is building the dynamics of each flow in a decentralized algorithm format. Decentralized regulation has solved congestion problems for internet data flow, traffic flow, epidemiology, and as demonstrated in this study can solve the problem of Natural Gas congestion. A mathematical description is provided for how decentralized regulation leads to globally optimized network flow. Furthermore, the dynamical principles and decentralized algorithm are applied to a case study of the Fluxys Belgium Natural Gas Network.
Dynamic pathway modeling: feasibility analysis and optimal experimental design.
Maiwald, Thomas; Kreutz, Clemens; Pfeifer, Andrea C; Bohl, Sebastian; Klingmüller, Ursula; Timmer, Jens
2007-12-01
A major challenge in systems biology is to evaluate the feasibility of a biological research project prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. Given a null hypothesis and alternative model, as well as laboratory constraints like observable players, sample size, noise level, and stimulation options, we suggest a method to obtain a list of required experiments in order to significantly reject the null hypothesis model M0 if a specified alternative model MA is realized. For this purpose, we estimate the power to detect a violation of M0 by means of Monte Carlo simulations. Iteratively, the power is maximized over all feasible stimulations of the system using multi-experiment fitting, leading to an optimal combination of experimental settings to discriminate the null hypothesis and alternative model. We prove the importance of simultaneous modeling of combined experiments with quantitative, highly sampled in vivo measurements from the Jak/STAT5 signaling pathway in fibroblasts, stimulated with erythropoietin (Epo). Afterwards we apply the presented iterative experimental design approach to the Jak/STAT3 pathway of primary hepatocytes stimulated with IL-6. Our approach offers the possibility of deciding which scientific questions can be answered based on existing laboratory constraints. To be able to concentrate on feasible questions on account of inexpensive computational simulations yields not only enormous cost and time saving, but also helps to specify realizable, systematic research projects in advance. PMID:18033750
Optimizing electromagnetic induction sensors for dynamic munitions classification surveys
NASA Astrophysics Data System (ADS)
Miller, Jonathan S.; Keranen, Joe; Schultz, Gregory
2014-06-01
Standard protocol for detection and classification of Unexploded Ordnance (UXO) comprises a two-step process that includes an initial digital geophysical mapping (DGM) survey to detect magnetic field anomalies followed by a cued survey at each anomaly location that enables classification of these anomalies. The initial DGM survey is typically performed using a low resolution single axis electromagnetic induction (EMI) sensor while the follow-up cued survey requires revisiting each anomaly location with a multi-axis high resolution EMI sensor. The DGM survey comprises data collection in tightly spaced transects over the entire survey area. Once data collection in this area is complete, a threshold analysis is applied to the resulting magnetic field anomaly map to identify anomalies corresponding to potential targets of interest (TOI). The cued sensor is deployed in static mode where this higher resolution sensor is placed over the location of each anomaly to record a number of soundings that may be stacked and averaged to produce low noise data. These data are of sufficient quality to subsequently classify the object as either TOI or clutter. While this approach has demonstrated success in producing effective classification of UXO, conducting successive surveys is time consuming. Additionally, the low resolution of the initial DGM survey often produces errors in the target picking process that results in poor placement of the cued sensor and often requires several revisits to the anomaly location to ensure adequate characterization of the target space. We present data and test results from an advanced multi-axis EMI sensor optimized to provide both detection and classification from a single survey. We demonstrate how the large volume of data from this sensor may be used to produce effective detection and classification decisions while only requiring one survey of the munitions response area.
Shu, Chuan-Cun; Edwalds, Melanie; Shabani, Alireza; Ho, Tak-San; Rabitz, Herschel
2015-07-01
The efficacy of optimal control of quantum dynamics depends on the topology and associated local structure of the underlying control landscape defined as the objective as a function of the control field. A commonly studied control objective involves maximization of the transition probability for steering the quantum system from one state to another state. This paper invokes landscape Hessian analysis performed at an optimal solution to gain insight into the controlled dynamics, where the Hessian is the second-order functional derivative of the control objective with respect to the control field. Specifically, we consider a quantum system composed of coupled primary and secondary subspaces of energy levels with the initial and target states lying in the primary subspace. The primary and secondary subspaces may arise in various scenarios, for example, respectively, as sub-manifolds of ground and excited electronic states of a poly-atomic molecule, with each possessing a set of rotational-vibrational levels. The control field may engage the system through electric dipole transitions that occur either (I) only in the primary subspace, (II) between the two subspaces, or (III) only in the secondary subspace. Important insights about the resultant dynamics in each case are revealed in the structural patterns of the corresponding Hessian. The Fourier spectrum of the Hessian is shown to often be complementary to mechanistic insights provided by the optimal control field and population dynamics. PMID:26119871
Reduced-Order Model for Dynamic Optimization of Pressure Swing Adsorption
Agarwal, Anshul (Carnegie Mellon Univ., Pittsburgh, PA); Biegler, L.T. (Carnegie Mellon Univ., Pittsburgh, PA); Zitney, S.E.
2007-11-01
The last few decades have seen a considerable increase in the applications of adsorptive gas separation technologies, such as pressure swing adsorption (PSA). From an economic and environmental point of view, hydrogen separation and carbon dioxide capture from flue gas streams are the most promising applications of PSA. With extensive industrial applications, there is a significant interest for an efficient modeling, simulation, and optimization strategy. However, the design and optimization of the PSA processes have largely remained an experimental effort because of the complex nature of the mathematical models describing practical PSA processes. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together and high nonlinearities arising from non-isothermal effects. The computational effort required to solve such systems is usually quite expensive and prohibitively time consuming. Besides this, stringent product specifications, required by many industrial processes, often lead to convergence failures of the optimizers. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either design or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Sophisticated optimization strategies have been developed and applied to PSA systems with significant improvement in the performance of the process. However, most of these approaches have been quite time consuming. This gives a strong motivation to develop cost-efficient and robust optimization strategies for PSA processes. Moreover, in case of flowsheet optimization, if dynamic PSA models are incorporated with other steady state models in the flowsheet then it will require much faster approaches for integrated optimization.
Coherent open-loop optimal control of light-harvesting dynamics
Caruso, Filippo; Calarco, Tommaso; Huelga, Susana F; Plenio, Martin B
2011-01-01
We apply theoretically open-loop quantum optimal control techniques to provide methods for the verification of various quantum coherent transport mechanisms in natural and artificial light-harvesting complexes under realistic experimental constraints. We demonstrate that optimally shaped laser pulses allow to faithfully prepare the photosystem in specified initial states (such as localized excitation or coherent superposition, i.e. propagating and non-propagating states) and to probe efficiently the dynamics. These results provide a path towards the discrimination of the different transport pathways and to the characterization of environmental properties, enhancing our understanding of the role that coherent processes may play in biological complexes.
Coherent open-loop optimal control of light-harvesting dynamics
Filippo Caruso; Simone Montangero; Tommaso Calarco; Susana F. Huelga; Martin B. Plenio
2011-03-04
We apply theoretically open-loop quantum optimal control techniques to provide methods for the verification of various quantum coherent transport mechanisms in natural and artificial light-harvesting complexes under realistic experimental constraints. We demonstrate that optimally shaped laser pulses allow to faithfully prepare the photosystem in specified initial states (such as localized excitation or coherent superposition, i.e. propagating and non-propagating states) and to probe efficiently the dynamics. These results provide a path towards the discrimination of the different transport pathways and to the characterization of environmental properties, enhancing our understanding of the role that coherent processes may play in biological complexes.
NASA Astrophysics Data System (ADS)
Heri?anu, Nicolae; Marinca, Vasile
2012-09-01
A version of the optimal homotopy perturbation method (OHPM) is applied in this study to derive highly accurate analytical expressions for the solutions to a non-conservative dynamical system of a rotating electrical machine. The main advantage of this procedure consists of providing us with a convenient and rigorous way to control the approximate solutions by means of some initially unknown parameters which are optimally determined later. Comparisons with numerical results reveal an excellent agreement, which demonstrates the effectiveness of the proposed method in analyzing non-conservative oscillators.
Optimal placement of excitations and sensors for verification of large dynamical systems
NASA Technical Reports Server (NTRS)
Salama, M.; Rose, T.; Garba, J.
1987-01-01
The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.
Analysis and formulation of a class of complex dynamic optimization problems
NASA Astrophysics Data System (ADS)
Kameswaran, Shivakumar
The Direct Transcription approach, also known as the direct simultaneous approach, is a widely used solution strategy for the solution of dynamic optimization problems involving differential-algebraic equations (DAEs). Direct transcription refers to the procedure of approximating the infinite dimensional problem by a finite dimensional one, which is then solved using a nonlinear programming (NLP) solver tailored to large-scale problems. Systems governed by partial differential equations (PDEs) can also be handled by spatially discretizing the PDEs to convert them to a system of DAEs. The objective of this thesis is firstly to ensure that direct transcription using Radau collocation is provably correct, and secondly to widen the applicability of the direct simultaneous approach to a larger class of dynamic optimization and optimal control problems (OCPs). This thesis aims at addressing these issues using rigorous theoretical tools and/or characteristic examples, and at the same time use the results for solving large-scale industrial applications to realize the benefits. The first part of this work deals with the analysis of convergence rates for direct transcription of unconstrained and final-time equality constrained optimal control problems. The problems are discretized using collocation at Radau points. Convergence is analyzed from an NLP/matrix-algebra perspective, which enables the prediction of the conditioning of the direct transcription NLP as the mesh size becomes finer. Several convergence results are presented along with tests on numerous example problems. These convergence results lead to an adjoint estimation procedure given the Lagrange multipliers for the large-scale NLP. The work also reveals the role of process control concepts such as controllability on the convergence analysis, and provides a very important link between control and optimization inside the framework of dynamic optimization. As an effort to extend the applicability of the direct simultaneous approach to a wider class of problems, a PDE-constrained optimal control problem, the spatial discretization of which results in a DAE-constrained problem with an arbitrarily high-index inequality constraint, is studied. Optimal control problems with high-index path constraints are very hard to solve, numerically. Contrary to the intuitive belief that the direct transcription approach would not work for the high-index optimal control problem, an analysis is performed to show that NLP-based methods have flexibility with respect to constraint qualifications, and this can be put to use in the context of high-index inequality path-constrained problems to obtain meaningful solutions. (Abstract shortened by UMI.)
An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments
NASA Astrophysics Data System (ADS)
Kang, Yuncheol; Prabhu, Vittal
The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.
Statistical Stability Analysis for Particle Swarm Optimization Dynamics with Random Coefficients
NASA Astrophysics Data System (ADS)
Koguma, Yuji; Aiyhosi, Eitaro
Particle Swarm Optimization (PSO), a meta-heuristic global optimization method, has attracted special interest for its simple algorithm and high searching ability. The updating formula of PSO involves coefficients with random numbers as parameters to enhance diversification ability in searching for the global optimum. However, the randomness makes stability of the searching points difficult to be analyzed mathematically, and the users need to adjust the parameter values by trial and error. In this paper, stability of the stochastic dynamics of PSO is analyzed mathematically and exact stability condition taking the randomness into consideration is presented with an index “statistical eigenvalue”, which is a new concept to evaluate the degree of the stability of PSO dynamics. Accuracy and effectiveness of the proposed stability discrimination using the presented index are certified in numerical simulation for simple examples.
Human motion planning based on recursive dynamics and optimal control techniques
NASA Technical Reports Server (NTRS)
Lo, Janzen; Huang, Gang; Metaxas, Dimitris
2002-01-01
This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.
Sklarz, Shlomo E.; Tannor, David J.; Khaneja, Navin [Department of Chemical Physics, Weizmann Institute of Science, Rehovot, 76100 (Israel); Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138 (United States)
2004-05-01
We study the problem of optimal control of dissipative quantum dynamics. Although under most circumstances dissipation leads to an increase in entropy (or a decrease in purity) of the system, there is an important class of problems for which dissipation with external control can decrease the entropy (or increase the purity) of the system. An important example is laser cooling. In such systems, there is an interplay of the Hamiltonian part of the dynamics, which is controllable, and the dissipative part of the dynamics, which is uncontrollable. The strategy is to control the Hamiltonian portion of the evolution in such a way that the dissipation causes the purity of the system to increase rather than decrease. The goal of this paper is to find the strategy that leads to maximal purity at the final time. Under the assumption that Hamiltonian control is complete and arbitrarily fast, we provide a general framework by which to calculate optimal cooling strategies. These assumptions lead to a great simplification, in which the control problem can be reformulated in terms of the spectrum of eigenvalues of {rho}, rather than {rho} itself. By combining this formulation with the Hamilton-Jacobi-Bellman theorem we are able to obtain an equation for the globally optimal cooling strategy in terms of the spectrum of the density matrix. For the three-level {lambda} system, we provide a complete analytic solution for the optimal cooling strategy. For this system it is found that the optimal strategy does not exploit system coherences and is a 'greedy' strategy, in which the purity is increased maximally at each instant.
NASA Astrophysics Data System (ADS)
Liu, Wenyuan; Wang, Chao; Li, Yanbin; Lao, Yuyang; Han, Yongjian; Guo, Guang-Can; Zhao, Yong-Hua; He, Lixin
2015-03-01
Tensor network states (TNS) methods combined with the Monte Carlo (MC) technique have been proven a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling technique, by mapping the energy function of the TNS to that of a classical mechanical system. The method is expected to effectively avoid local minima. We make benchmark tests on a 1D Hubbard model based on matrix product states (MPS) and a Heisenberg J1–J2 model on square lattice based on string bond states (SBS). The results show that the optimization method is robust and efficient compared to the existing results.
Kamyad, Ali Vahidian; Heydari, Ali Akbar; Heydari, Aghileh
2014-01-01
Hepatitis B virus (HBV) infection is a worldwide public health problem. In this paper, we study the dynamics of hepatitis B virus (HBV) infection which can be controlled by vaccination as well as treatment. Initially we consider constant controls for both vaccination and treatment. In the constant controls case, by determining the basic reproduction number, we study the existence and stability of the disease-free and endemic steady-state solutions of the model. Next, we take the controls as time and formulate the appropriate optimal control problem and obtain the optimal control strategy to minimize both the number of infectious humans and the associated costs. Finally at the end numerical simulation results show that optimal combination of vaccination and treatment is the most effective way to control hepatitis B virus infection. PMID:24812572
Liu, Wenyuan; Wang, Chao; Li, Yanbin; Lao, Yuyang; Han, Yongjian; Guo, Guang-Can; Zhao, Yong-Hua; He, Lixin
2015-03-01
Tensor network states (TNS) methods combined with the Monte Carlo (MC) technique have been proven a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling technique, by mapping the energy function of the TNS to that of a classical mechanical system. The method is expected to effectively avoid local minima. We make benchmark tests on a 1D Hubbard model based on matrix product states (MPS) and a Heisenberg J1-J2 model on square lattice based on string bond states (SBS). The results show that the optimization method is robust and efficient compared to the existing results. PMID:25654245
P. Hasanpor Divshali; S. H. Hosseinian; E. Nasr Azadani; M. Abedi
In a deregulated environment of power systems, the transmission networks are often operated close to their maximum capacity.\\u000a Besides, the independent system operator must operate the system to satisfy its dynamic stability constraints under credible\\u000a contingencies. In this paper, a novel technique based on iterative stability constrained Optimum Power Flow is proposed. Particle\\u000a Swarm Optimization methodology is employed to maximize
Balasubramonian, Rajeev (Sandy, UT); Dwarkadas, Sandhya (Rochester, NY); Albonesi, David (Ithaca, NY)
2012-01-24
In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.
Dynamic Optimality and MultiSplay Trees 1 Daniel Dominic Sleator and Chengwen Chris Wang
is nearly dynamically optimal -- its competitive ratio is O(log log n) instead of a constant. Unfortunately, for many access patterns, tango is worse than other BST algorithms by a factor of log log n. In this paper that multiÂsplay trees are O(log log n)Âcompetitive, and amortized O(log n). This is the first BST data
Optimal discrete-time dynamic output-feedback design - A w-domain approach
NASA Technical Reports Server (NTRS)
Ha, Cheolkeun; Ly, Uy-Loi
1991-01-01
An alternative method for optimal digital control design is described in this paper. The method is based on the usage of the w-transform and has many attractive design features. One of these is its immediate connection with frequency loop-shaping techniques that are now popular and effective for multivariable control synthesis in continuous-time domain. Furthermore, any design algorithms originally developed for continuous-time systems can now be immediately extended to the discrete-time domain. The main results presented in this paper are the exact problem formulation and solution of an optimal discrete-time dynamic output-feedback design in the w-domain involving a quadratic performance index to random disturbances. In addition, necessary conditions for optimality are obtained for the numerical solution of the optimal output-feedback compensator design. A numerical example is presented illustrating its application to the design of a low-order dynamic compensator in a stability augmentation system of a commercial transport.
NASA Astrophysics Data System (ADS)
Helbing, Dirk; Schönhof, Martin; Kern, Daniel
2002-06-01
The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified methods of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems and for information service providers. One of the promising fields of application is traffic optimization.
Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches
Bartumeus, Frederic; Raposo, Ernesto P.; Viswanathan, Gandhimohan M.; da Luz, Marcos G. E.
2014-01-01
Recent theoretical developments had laid down the proper mathematical means to understand how the structural complexity of search patterns may improve foraging efficiency. Under information-deprived scenarios and specific landscape configurations, Lévy walks and flights are known to lead to high search efficiencies. Based on a one-dimensional comparative analysis we show a mechanism by which, at random, a searcher can optimize the encounter with close and distant targets. The mechanism consists of combining an optimal diffusivity (optimally enhanced diffusion) with a minimal diffusion constant. In such a way the search dynamics adequately balances the tension between finding close and distant targets, while, at the same time, shifts the optimal balance towards relatively larger close-to-distant target encounter ratios. We find that introducing a multiscale set of reorientations ensures both a thorough local space exploration without oversampling and a fast spreading dynamics at the large scale. Lévy reorientation patterns account for these properties but other reorientation strategies providing similar statistical signatures can mimic or achieve comparable efficiencies. Hence, the present work unveils general mechanisms underlying efficient random search, beyond the Lévy model. Our results suggest that animals could tune key statistical movement properties (e.g. enhanced diffusivity, minimal diffusion constant) to cope with the very general problem of balancing out intensive and extensive random searching. We believe that theoretical developments to mechanistically understand stochastic search strategies, such as the one here proposed, are crucial to develop an empirically verifiable and comprehensive animal foraging theory. PMID:25216191
NASA Astrophysics Data System (ADS)
Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.
2015-05-01
The most widely used community weather forecast and research model in the world is the Weather Research and Forecast (WRF) model. Two distinct varieties of WRF exist. The one we are interested is the Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we optimize a meridional (north-south direction) advection subroutine for Intel Xeon Phi coprocessor. Advection is of the most time consuming routines in the ARW dynamics core. It advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.2x.
Optimized dynamic contrast-enhanced cone-beam CT for target visualization during liver SBRT
NASA Astrophysics Data System (ADS)
Jones, Bernard L.; Altunbas, Cem; Kavanagh, Brian; Schefter, Tracey; Miften, Moyed
2014-03-01
The pharmacokinetic behavior of iodine contrast agents makes it difficult to achieve significant enhancement during contrast-enhanced cone-beam CT (CE-CBCT). This study modeled this dynamic behavior to optimize CE-CBCT and improve the localization of liver lesions for SBRT. We developed a model that allows for controlled study of changing iodine concentrations using static phantoms. A projection database consisting of multiple phantom images of differing iodine/scan conditions was built. To reconstruct images of dynamic hepatic concentrations, hepatic contrast enhancement data from conventional CT scans were used to re-assemble the projections to match the expected amount of contrast. In this way the effect of various parameters on image quality was isolated, and using our dynamic model we found parameters for iodine injection, CBCT scanning, and injection/scanning timing which optimize contrast enhancement. Increasing the iodine dose, iodine injection rate, and imaging dose led to significant increases in signal-to-noise ratio (SNR). Reducing the CBCT imaging time also increased SNR, as the image can be completed before the iodine exits the liver. Proper timing of image acquisition played a significant role, as a 30 second error in start time resulted in a 40% SNR decrease. The effect of IV contrast is severely degraded in CBCT, but there is promise that, with optimization of the injection and scan parameters to account for iodine pharmacokinetics, CE-CBCT which models venous-phase blood flow kinetics will be feasible for accurate localization of liver lesions.
Criteria for Optimizing Cortical Hierarchies with Continuous Ranges
Krumnack, Antje; Reid, Andrew T.; Wanke, Egon; Bezgin, Gleb; Kötter, Rolf
2009-01-01
In a recent paper (Reid et al., 2009) we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that define the hierarchy was minimized using linear optimization. In the short time since publication of that paper we noticed that many colleagues misinterpreted the meaning of the term “optimal hierarchy”. In particular, a majority of them were under the impression that there was perhaps only one optimal hierarchy, but a substantial difficulty in finding that one. However, there is not only more than one optimal hierarchy but also more than one option for defining optimality. Continuing the line of this work we look at additional options for optimizing the visual hierarchy: minimizing the number of violated constraints and minimizing the maximal size of a constraint violation using linear optimization and mixed integer programming. The implementation of both optimization criteria is explained in detail. In addition, using constraint sets based on the data from Felleman and Van Essen (1991), optimal hierarchies for the visual network are calculated for both optimization methods. PMID:20407634
Hartmann, András; Lemos, João M; Vinga, Susana
2015-08-01
The aim of inverse modeling is to capture the systems? dynamics through a set of parameterized Ordinary Differential Equations (ODEs). Parameters are often required to fit multiple repeated measurements or different experimental conditions. This typically leads to a multi-objective optimization problem that can be formulated as a non-convex optimization problem. Modeling of glucose utilization of Lactococcus lactis bacteria is considered using in vivo Nuclear Magnetic Resonance (NMR) measurements in perturbation experiments. We propose an ODE model based on a modified time-varying exponential decay that is flexible enough to model several different experimental conditions. The starting point is an over-parameterized non-linear model that will be further simplified through an optimization procedure with regularization penalties. For the parameter estimation, a stochastic global optimization method, particle swarm optimization (PSO) is used. A regularization is introduced to the identification, imposing that parameters should be the same across several experiments in order to identify a general model. On the remaining parameter that varies across the experiments a function is fit in order to be able to predict new experiments for any initial condition. The method is cross-validated by fitting the model to two experiments and validating the third one. Finally, the proposed model is integrated with existing models of glycolysis in order to reconstruct the remaining metabolites. The method was found useful as a general procedure to reduce the number of parameters of unidentifiable and over-parameterized models, thus supporting feature selection methods for parametric models. PMID:25248561
NASA Astrophysics Data System (ADS)
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
H? optimization of dynamic vibration absorber variant for vibration control of damped linear systems
NASA Astrophysics Data System (ADS)
Chun, Semin; Lee, Youngil; Kim, Tae-Hyoung
2015-01-01
This study focuses on the H? optimal design of a dynamic vibration absorber (DVA) variant for suppressing high-amplitude vibrations of damped primary systems. Unlike traditional DVA configurations, the damping element in this type of DVA is connected directly to the ground instead of the primary mass. First, a thorough graphical analysis of the variations in the maximum amplitude magnification factor depending on two design parameters, natural frequency and absorber damping ratios, is performed. The results of this analysis clearly show that any fixed-points-theory-based conventional method could provide, at best, only locally but not globally optimal parameters. Second, for directly handling the H? optimization for its optimal design, a novel meta-heuristic search engine, called the diversity-guided cyclic-network-topology-based constrained particle swarm optimization (Div-CNT-CPSO), is developed. The variant DVA system developed using the proposed Div-CNT-CPSO scheme is compared with those reported in the literature. The results of this comparison verified that the proposed system is better than the existing methods for suppressing the steady-state vibration amplitude of a controlled primary system.
2011-01-01
Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology. PMID:21989196
Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Castro, Joseph Pete Jr.; Giunta, Anthony Andrew
2006-01-01
Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation.
NASA Technical Reports Server (NTRS)
Whiffen, Gregory J.
2006-01-01
Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.
NASA Astrophysics Data System (ADS)
Yan, Yiming; Zhang, Ye; Gao, Fengjiao
2012-12-01
This article proposes a `dynamic' artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic `activity' factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including `kernel parameter', `cost factor', etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.
Dynamic optimization of CELSS crop photosynthetic rate by computer-assisted feedback control.
Chun, C; Mitchell, C A
1997-01-01
A procedure for dynamic optimization of net photosynthetic rate (Pn) for crop production in Controlled Ecological Life-Support Systems (CELSS) was developed using leaf lettuce as a model crop. Canopy Pn was measured in real time and fed back for environmental control. Setpoints of photosynthetic photon flux (PPF) and CO2 concentration for each hour of the crop-growth cycle were decided by computer to reach a targeted Pn each day. Decision making was based on empirical mathematical models combined with rule sets developed from recent experimental data. Comparisons showed that dynamic control resulted in better yield per unit energy input to the growth system than did static control. With comparable productivity parameters and potential for significant energy savings, dynamic control strategies will contribute greatly to the sustainability of space-deployed CELSS. PMID:11542561
Minnesota, University of
Region Monitoring for Local Phase Detection in Dynamic Optimization Systems Abhinav Das, Jiwei Lu by aggregating the performance of individually optimized regions can be misled by some re- gions impacting the candidate regions for op- timization. By associating phase detection to individual re- gions we can isolate
Matias, Yossi
On the optimality of parsing in dynamic dictionary based data compression Yossi Matias Suleyman with respect to the dictionary in use (2) a one- lookahead greedy parsing scheme obtains optimality with re- spect to any dictionary construction schemes that satisfy the pre x property and (3) there is a data
Computational fluid dynamics based aerodynamic optimization of the wind tunnel primary nozzle
NASA Astrophysics Data System (ADS)
Jan, Kolá?; Václav, Dvo?ák
2012-06-01
The aerodynamic shape optimization of the supersonic flat nozzle is the aim of proposed paper. The nozzle discussed, is applied as a primary nozzle of the inlet part of supersonic wind tunnel. Supersonic nozzles of the measure area inlet parts need to guarantee several requirements of flow properties and quality. Mach number and minimal differences between real and required velocity and turbulence profiles at the nozzle exit are the most important parameters to meet. The aerodynamic shape optimization of the flat 2D nozzle in Computational Fluid Dynamics (CFD) is employed to reach as uniform exit velocity profile as possible, with the mean Mach number 1.4. Optimization process does not use any of standard routines of global or local optimum searching. Instead, newly formed routine, which exploits shape-based oriented sequence of nozzles, is used to research within whole discretized parametric space. The movement within optimization process is not driven by gradient or evolutionary too, instead, the Path of Minimal Shape Deformation is followed. Dynamic mesh approach is used to deform the shape and mesh from the actual nozzle to the subsequent one. Dynamic deformation of mesh allows to speed up whole converging process as an initialization of flow at the newly formed mesh is based on afore-computed shape. Shape-based similarity query in field of supersonic nozzles is discussed and applied. Evolutionary technique with genetic algorithm is used to search for minimal deformational path. As a result, the best variant from the set of solved shapes is analyzed at the base of momentum coefficient and desired Mach number at the nozzle exit.
Applying dynamic wake models to large swirl velocities for optimal propellers
NASA Astrophysics Data System (ADS)
Makinen, Stephen M.
The dynamic wake model is applied to the optimal propeller systems originally studied by the classic aerodynamicists: Betz, Prandtl and Goldstein. Several modified forms of the model are theoretically developed to extend the applicable range to flight conditions with a large swirl velocity component. Dynamic wake model calculations accurately predict the inflow behavior for helicopter rotors, including axial flow for large tip-speed ratios, (OR/V infinity) ? 20. The swirl velocity is a prominent component for small tip-speed ratios (?5), typical of forward flight for tiltrotor craft such as the V-22 Osprey and the BA609. Dynamic wake calculation results are compared to the closed-form solutions by Prandtl and Goldstein. The exact and approximate solutions correlate strongly for infinite blade cases and finite blade cases with a large tip-speed ratio. The original form of the He-Peters and Morillo-Peters dynamic wake models converge poorly for small tip-speed ratios, due to neglect of the swirl velocity. Derivations are presented for several adaptations of the models to account for the large apparent mass at the inboard blade region. A best modified form is chosen and the associated empirical factor is optimized to correlate well with Prandtl's solution. Error norms for the original and modified forms of the dynamic wake model are presented for propellers of various number of blades and a range of tip-speed ratios. The Goldstein solution is also studied in depth and conclusions are drawn for improving the dynamic wake model.
Dynamic biclustering of microarray data by multi-objective immune optimization
2011-01-01
Abstract Background Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem. Results Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. Conclusions The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence. PMID:21989068
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-08-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
NASA Astrophysics Data System (ADS)
Perez, Ruben Eduardo
The emerging fly-by-wire and fly-by-light technologies increase the possibility of producing aircraft with excellent handling qualities and increased performance across the flight envelope. As a result, flight dynamics and control have become an important discipline in the design of air vehicles; where it is desired to take into account the dynamic characteristics and automatic control capabilities at the earliest stages of design. Traditionally, very limited considerations have been made at the early conceptual stage regarding this discipline. Some simplified methodologies used to size the control surfaces (i.e. horizontal and vertical tail surfaces) result in sub-optimal designs due to their inability to capture the interactions among the sizing of control surfaces, their control effectors (i.e. elevator and rudder) and it systems, and their effect on the general dynamic behaviour of the aircraft. Such designs have great limitations on control and handling, which lead to costly design modifications at the later stages of design. This research presents a methodology that enables flight dynamics and control integration at the conceptual design stage using multidisciplinary design optimization. It finds feasible aircraft configurations which meet specified mission requirements concurrently with the stability, control, and handling quality requirements at multiple flight conditions within the flight envelope. Furthermore, the proposed methodology exploits two different control integration strategies. The first one allows for individualized control system design at each flight phase, while the second one focuses on simultaneous stabilization and optimization using one single controller. The application of the methodology leads to designs that exploit active control interactions and have better performance and flying characteristics than the traditional sizing process over a broad range of aircraft sizes.
Pharmacokinetic modeling of dynamic MR images using a simulated annealing-based optimization
NASA Astrophysics Data System (ADS)
Sawant, Amit R.; Reece, John H.; Reddick, Wilburn E.
2000-04-01
The aim of this work was to use dynamic contrast enhanced MR image (DEMRI) data to generate 'parameter images' which provide functional information about contrast agent access, in bone sarcoma. A simulated annealing based technique was applied to optimize the parameters of a pharmacokinetic model used to describe the kinetics of the tissue response during and after intravenous infusion of a paramagnetic contrast medium, Gd-DTPA. Optimization was performed on a pixel by pixel basis so as to minimize the sum of square deviations of the calculated values from the values obtained experimentally during dynamic contrast enhanced MR imaging. A cost function based on a priori information was introduced during the annealing procedure to ensure that the values obtained were within the expected ranges. The optimized parameters were used in the model to generate parameter images, which reveal functional information that is normally not visible in conventional Gd-DTPA enhanced MR images. This functional information, during and upon completion of pre-operative chemotherapy, is useful in predicting the probability of disease free survival.
Fast optimization of binary clusters using a novel dynamic lattice searching method
Wu, Xia Cheng, Wen
2014-09-28
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd){sub 79} clusters with DFT-fit parameters of Gupta potential.
Fast optimization of binary clusters using a novel dynamic lattice searching method
NASA Astrophysics Data System (ADS)
Wu, Xia; Cheng, Wen
2014-09-01
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.
Vegetal Optimality and Macro-Scale Dynamic Vegetation - Scaling from Leaf to Landscape
NASA Astrophysics Data System (ADS)
Quebbeman, J.; Ramirez, J. A.
2014-12-01
Macro-scale spatially distributed hydrologic models require extensive parameterization of both soil and vegetal properties. Proper parameterization of vegetation is critical for understanding vegetal response to hydro-climatic variability, as vegetation provides a key feedback to climate. A common practice for Dynamic Global Vegetation Models is to use plant functional types (PFTs), which limit vegetation to discrete classes. We present a physically based long-term macro-scale coupled vegetation and hydrology model capable of responding dynamically to climate variability, and parameterize it assuming vegetal optimality hypotheses. We hypothesize that canopy scale vegetation will adopt a strategy that maximizes the expected net assimilation, minus photosynthetic system construction and maintenance costs, over an annual basis. We perform stochastic multi-decadal simulations to estimate the expected fitness for a unique vegetal parameterization and water use strategy. As a result, optimal parameter sets are defined, which can be used instead of a PFT characterization of land cover. Estimates of evaporation, transpiration and gross primary production obtained using the optimal parameter sets over a range of climates are then compared against FLUXNET data.
Dynamics of hepatitis C under optimal therapy and sampling based analysis
NASA Astrophysics Data System (ADS)
Pachpute, Gaurav; Chakrabarty, Siddhartha P.
2013-08-01
We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes infected hepatocyte levels, virion population and side-effects of the drug(s). The optimal therapies for both the models show an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the viral load, whereas the efficacy drops after hepatocyte levels are restored. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (indicated by drop in viral load below detection levels) in case of combination therapy (72%) as compared to monotherapy (57%). Statistical tests performed to study correlations between sample parameters and time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens.
Van Derlinden, E; Bernaerts, K; Van Impe, J F
2008-11-30
Prediction of the microbial growth rate as a response to changing temperatures is an important aspect in the control of food safety and food spoilage. Accurate model predictions of the microbial evolution ask for correct model structures and reliable parameter values with good statistical quality. Given the widely accepted validity of the Cardinal Temperature Model with Inflection (CTMI) [Rosso, L., Lobry, J. R., Bajard, S. and Flandrois, J. P., 1995. Convenient model to describe the combined effects of temperature and pH on microbial growth, Applied and Environmental Microbiology, 61: 610-616], this paper focuses on the accurate estimation of its four parameters (T(min), T(opt), T(max) and micro(opt)) by applying the technique of optimal experiment design for parameter estimation (OED/PE). This secondary model describes the influence of temperature on the microbial specific growth rate from the minimum to the maximum temperature for growth. Dynamic temperature profiles are optimized within two temperature regions ([15 degrees C, 43 degrees C] and [15 degrees C, 45 degrees C]), focusing on the minimization of the parameter estimation (co)variance (D-optimal design). The optimal temperature profiles are implemented in a computer controlled bioreactor, and the CTMI parameters are identified from the resulting experimental data. Approximately equal CTMI parameter values were derived irrespective of the temperature region, except for T(max). The latter could only be estimated accurately from the optimal experiments within [15 degrees C, 45 degrees C]. This observation underlines the importance of selecting the upper temperature constraint for OED/PE as close as possible to the true T(max). Cardinal temperature estimates resulting from designs within [15 degrees C, 45 degrees C] correspond with values found in literature, are characterized by a small uncertainty error and yield a good result during validation. As compared to estimates from non-optimized dynamic experiments, more reliable CTMI parameter values were obtained from the optimal experiments within [15 degrees C, 45 degrees C]. PMID:18835500
NASA Astrophysics Data System (ADS)
Wang, Hongtao; Zhang, Guizhen; Zheng, Shijie
2013-10-01
An innovative method based on dynamic particle swarm optimization (DPSO) algorithm is presented to demodulate the strain profile along a fiber Bragg grating (FBG) from its reflection spectrum, which is calculated by using the modified transfer matrix method. To improve the optimization performances of algorithm itself, the inertia weight of the DPSO algorithm is adjusted dynamically according to the distance between the individual particle and the global optimal particle in the current population. Then the numerical simulation and experimental verification of the reconstruction of nonuniform strain profiles are comprehensively carried out. Both the simulation examples and experimental results verify the feasibility and validity of the present method.
Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation.
Jing, Liang; Chen, Bing; Zhang, Baiyu; Li, Pu
2015-09-15
UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control. PMID:26043376
Empirical prediction of climate dynamics: optimal models, derived from time series
NASA Astrophysics Data System (ADS)
Mukhin, D.; Loskutov, E. M.; Gavrilov, A.; Feigin, A. M.
2013-12-01
The new empirical method for prediction of climate indices by the analysis of climatic fields' time series is suggested. The method is based on construction of prognostic models of evolution operator (EO) in low-dimensional subspaces of system's phase space. One of the main points of suggested analysis is reconstruction of appropriate basis of dynamical variables (predictors) from spatially distributed data: different ways of data decomposition are discussed in the report including EOFs, MSSA and other relevant data rotations. We consider the models of different complexity for EO reconstruction, from linear statistical models of particular indices to more complex artificial neural network (ANN) models of climatic patterns dynamics, which take the form of discrete random dynamical systems [1]. Very important problem, that always arises in empirical modeling approaches, is optimal model selection criterium: appropriate regularization procedure is needed to avoid overfitted model. Particulary, it includes finding the optimal structural parameters of the model such as dimension of variables vector, i.e. number of principal components used for modeling, number of states used for prediction, and number of parameters determining quality of approximation. In this report the minimal descriptive length (MDL) approach [2] is proposed for this purpose: the model providing most data compression is chosen. Results of application of suggested method to analysis of SST and SLP fields' time series [3] covering last 30-50 years are presented: predictions of different climate indices time series including NINO 3.4, MEI, PDO, NOA are shown. References: 1. Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series, Phys. Rev. E 85, 036216, 2012 2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009 3. IRI/LDEO (http://iridl.ldeo.columbia.edu/)
On PDE solution in transient optimization of gas networks
NASA Astrophysics Data System (ADS)
Steinbach, Marc C.
2007-06-01
Operative planning in gas distribution networks leads to large-scale mixed-integer optimization problems involving a hyperbolic PDE defined on a graph. We consider the NLP obtained under prescribed combinatorial decisions--or as relaxation in a branch-and-bound framework, addressing in particular the KKT systems arising in primal-dual interior methods. We propose a custom solution algorithm using sparse projections locally in time, based on the KKT systems' structural properties in space as induced by the discretized gas flow equations in combination with the underlying network topology. The numerical efficiency and accuracy of the algorithm are investigated, and detailed computational comparisons with a previously developed control space method and with the multifrontal solver MA27 are provided.
Inference for Optimal Dynamic Treatment Regimes using an Adaptive m-out-of-n Bootstrap Scheme
Chakraborty, Bibhas; Laber, Eric B.; Zhao, Yingqi
2013-01-01
Summary A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much more simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example. PMID:23845276
Modeling of CMM dynamic error based on optimization of neural network using genetic algorithm
NASA Astrophysics Data System (ADS)
Ying, Qu; Zai, Luo; Yi, Lu
2010-08-01
By analyzing the dynamic error of CMM, a model is established using BP neural network for CMM .The most important 5 input parameters which affect the dynamic error of CMM are approximate rate, length of rod, diameter of probe, coordinate values of X and coordinate values of Y. But the training of BP neural network can be easily trapped in local minimums and its training speed is slow. In order to overcome these disadvantages, genetic algorithm (GA) is introduced for optimization. So the model of GA-BP network is built up. In order to verify the model, experiments are done on the CMM of type 9158. Experimental results indicate that the entire optimizing capability of genetic algorithm is perfect. Compared with traditional BP network, the GA-BP network has better accuracy and adaptability and the training time is halved using GA-BP network. The average dynamic error can be reduced from 3.5?m to 0.7?m. So the precision is improved by 76%.
Optimal Control of Atomic, Molecular and Electron Dynamics with Tailored Femtosecond Laser Pulses
NASA Astrophysics Data System (ADS)
Brixner, Tobias; Pfeifer, Thomas; Gerber, Gustav; Wollenhaupt, Matthias; Baumert, Thomas
With the invention of the laser, the dream was realized to actively exert control over quantum systems. Active control over the dynamics of quantum mechanical systems is a fascinating perspective in modern physics. Cleavage and creation of predetermined chemical bonds, selective population transfer in atoms and molecules, and steering the dynamics of bound and free electrons have been important milestones along this way. A promising tool for this purpose is available with femtosecond laser technologies. In this chapter we review some of our work on adaptive femtosecond quantum control where a learning algorithm and direct experimental feedback signals are employed to optimize user-defined objectives. Femtosecond laser pulses are modified in frequency-domain pulse shapers, which apart from phase- and intensity-modulation can also modify the polarization state as a function of time. We will highlight the major advances in the field of optimal control by presenting our own illustrative experimental examples such as gas-phase and liquid-phase femtochemistry, control in weak and strong laser fields, and control of electron dynamics.
Optimization and application of a wrap-spring clutch to a dynamic knee-ankle-foot orthosis
Steven E. Irby; Kenton R. Kaufman; Roy W. Wirta; David H. Sutherland
1999-01-01
A dynamic knee-brace system (DKBS) has been designed which provides stance phase stability and swing phase freedom. A wrap-spring clutch controls knee flexion. Clutch optimization was performed minimizing clutch length. Kinematic tests on a normal subject using the DKBS document nearly normal dynamic knee flexion during swing (380 versus 530 for normal)
ERIC Educational Resources Information Center
Brusco, Michael J.; Stahl, Stephanie
2005-01-01
There are two well-known methods for obtaining a guaranteed globally optimal solution to the problem of least-squares unidimensional scaling of a symmetric dissimilarity matrix: (a) dynamic programming, and (b) branch-and-bound. Dynamic programming is generally more efficient than branch-and-bound, but the former is limited to matrices with…
NASA Technical Reports Server (NTRS)
Welstead, Jason; Crouse, Gilbert L., Jr.
2014-01-01
Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Optimizing the dynamic range extension of a radiochromic film dosimetry system.
Devic, Slobodan; Tomic, Nada; Soares, Christopher G; Podgorsak, Ervin B
2009-02-01
The authors present a radiochromic film dosimetry protocol for a multicolor channel radiochromic film dosimetry system consisting of the external beam therapy (EBT) model GAFCHROMIC film and the Epson Expression 1680 flat-bed document scanner. Instead of extracting only the red color channel, the authors are using all three color channels in the absorption spectrum of the EBT film to extend the dynamic dose range of the radiochromic film dosimetry system. By optimizing the dose range for each color channel, they obtained a system that has both precision and accuracy below 1.5%, and the optimized ranges are 0-4 Gy for the red channel, 4-50 Gy for the green channel, and above 50 Gy for the blue channel. PMID:19291981
NASA Astrophysics Data System (ADS)
Joung, Tae-Hwan; Sammut, Karl; He, Fangpo; Lee, Seung-Keon
2012-03-01
Autonomous Underwater Vehicles (AUVs) provide a useful means of collecting detailed oceano-graphic information. The hull resistance of an AUV is an important factor in determining the power requirements and range of the vehicle. This paper describes a procedure using Computational Fluid Dynamics (CFD) for determining the hull resistance of an AUV under development, for a given propeller rotation speed and within a given range of AUV velocities. The CFD analysis results reveal the distribution of the hydrodynamic values (velocity, pressure, etc.) around the AUV hull and its ducted propeller. The paper then proceeds to present a methodology for optimizing the AUV profile in order to reduce the total resistance. This paper demonstrates that shape optimization of conceptual designs is possible using the commercial CFD package contained in Ansys™. The optimum design to minimize the drag force of the AUV was identified for a given object function and a set of constrained design parameters
NASA Technical Reports Server (NTRS)
Nguyen, Howard; Willacy, Karen; Allen, Mark
2012-01-01
KINETICS is a coupled dynamics and chemistry atmosphere model that is data intensive and computationally demanding. The potential performance gain from using a supercomputer motivates the adaptation from a serial version to a parallelized one. Although the initial parallelization had been done, bottlenecks caused by an abundance of communication calls between processors led to an unfavorable drop in performance. Before starting on the parallel optimization process, a partial overhaul was required because a large emphasis was placed on streamlining the code for user convenience and revising the program to accommodate the new supercomputers at Caltech and JPL. After the first round of optimizations, the partial runtime was reduced by a factor of 23; however, performance gains are dependent on the size of the data, the number of processors requested, and the computer used.
Coherent control of plasma dynamics by feedback-optimized wavefront manipulationa)
NASA Astrophysics Data System (ADS)
He, Z.-H.; Hou, B.; Gao, G.; Lebailly, V.; Nees, J. A.; Clarke, R.; Krushelnick, K.; Thomas, A. G. R.
2015-05-01
Plasmas generated by an intense laser pulse can support coherent structures such as large amplitude wakefield that can affect the outcome of an experiment. We investigate the coherent control of plasma dynamics by feedback-optimized wavefront manipulation using a deformable mirror. The experimental outcome is directly used as feedback in an evolutionary algorithm for optimization of the phase front of the driving laser pulse. In this paper, we applied this method to two different experiments: (i) acceleration of electrons in laser driven plasma waves and (ii) self-compression of optical pulses induced by ionization nonlinearity. The manipulation of the laser wavefront leads to orders of magnitude improvement to electron beam properties such as the peak charge, beam divergence, and transverse emittance. The demonstration of coherent control for plasmas opens new possibilities for future laser-based accelerators and their applications.
Lin, Hung-Ming; Liu, Chien-Lin; Pan, Yung-Ning; Huang, Chang-Hung; Shih, Shih-Liang; Wei, Shun-Hwa; Chen, Chen-Sheng
2014-05-01
Surgeons often use spinal fixators to manage spinal instability. Dynesys (DY) is a type of dynamic fixator that is designed to restore spinal stability and to provide flexibility. The aim of this study was to design a new spinal fixator using topology optimization [the topology design (TD) system]. Here, we constructed finite element (FE) models of degenerative disc disease, DY, and the TD system. A hybrid-controlled analysis was applied to each of the three FE models. The rod structure of the topology optimization was modelled at a 39 % reduced volume compared with the rigid rod. The TD system was similar to the DY system in terms of stiffness. In contrast, the TD system reduced the cranial adjacent disc stress and facet contact force at the adjacent level. The TD system also reduced pedicle screw stresses in flexion, extension, and lateral bending. PMID:24737048
Optimizing the dynamic range extension of a radiochromic film dosimetry system
Devic, Slobodan; Tomic, Nada; Soares, Christopher G.; Podgorsak, Ervin B.
2009-02-15
The authors present a radiochromic film dosimetry protocol for a multicolor channel radiochromic film dosimetry system consisting of the external beam therapy (EBT) model GAFCHROMIC film and the Epson Expression 1680 flat-bed document scanner. Instead of extracting only the red color channel, the authors are using all three color channels in the absorption spectrum of the EBT film to extend the dynamic dose range of the radiochromic film dosimetry system. By optimizing the dose range for each color channel, they obtained a system that has both precision and accuracy below 1.5%, and the optimized ranges are 0-4 Gy for the red channel, 4-50 Gy for the green channel, and above 50 Gy for the blue channel.
A Dynamic Programming Algorithm for Optimal Design of Tidal Power Plants
NASA Astrophysics Data System (ADS)
Nag, B.
2013-03-01
A dynamic programming algorithm is proposed and demonstrated on a test case to determine the optimum operating schedule of a barrage tidal power plant to maximize the energy generation over a tidal cycle. Since consecutive sets of high and low tides can be predicted accurately for any tidal power plant site, this algorithm can be used to calculate the annual energy generation for different technical configurations of the plant. Thus an optimal choice of a tidal power plant design can be made from amongst different design configurations yielding the least cost of energy generation. Since this algorithm determines the optimal time of operation of sluice gate opening and turbine gates opening to maximize energy generation over a tidal cycle, it can also be used to obtain the annual schedule of operation of a tidal power plant and the minute-to-minute energy generation, for dissemination amongst power distribution utilities.
Optimization of Fluid Front Dynamics in Porous Media Using Rate Control: I. Equal Mobility Fluids
Sundaryanto, Bagus; Yortsos, Yanis C.
1999-10-18
In applications involving this injection of a fluid in a porous medium to displace another fluid, a main objective is the maximization of the displacement efficiency. For a fixed arrangement of injection and production points (sources and sinks), such optimization is possible by controlling the injection rate policy. Despite its practical relevance, however, this aspect has received scant attention in the literature. In this paper, a fundamental approach based on optimal control theory, for the case when the fluids are miscible, of equal viscosity and in the absence of dispersion and gravity effects. Both homogeneous and heterogeneous porous media are considered. From a fluid dynamics viewpoint, this is a problem in the deformation of material lines in porous media, as a function of time-varying injection rates.
Dynamic characteristics of mesh-based network control system under optimal resource circumstances
NASA Astrophysics Data System (ADS)
Huang, Dong; He, Bigui
2012-11-01
Network control system (NCS) has been presenting significance in industry and automation by supporting usability and convenience. However, for insufficiency for diversity of infrastructure and scalability, it has limitations in the field of flexibility for network architecture compared to the wireless mobile network. Hence, there shows the pressing need for tackling the existing network control system with control flow by wireless mesh network (WMN) with strong flexibility effectively. Consequently, for each control system, its stability is one of the prerequisites and criteria. In this paper, a novel optimization model was developed for planning the optimal resource utilization of wireless mesh network (WMN), and then the dynamic characteristics of mesh-based network control system are analyzed and discussed.
NASA Astrophysics Data System (ADS)
GOBBI, M.; MASTINU, G.
2001-08-01
A simple two-degree-of-freedom linear model is used to derive a number of analytical formulae describing the dynamic behaviour of passively suspended vehicles running on randomly profiled roads. Two different power spectral densities are considered for modelling the road irregularity. The derived analytical formulae can be used either during preliminary design or for other special purposes, especially when approximated results are acceptable.An optimization method, based on Multi-Objective Programming and Monotonicity analysis, is introduced and applied for the symbolic derivation of analytical formulae featuring the best compromise among conflicting performance indices pertaining to the vehicle suspension system, i.e., discomfort, road holding and working space. The optimal settings of the relevant vehicle suspension parameters (i.e., tyre radial stiffness, spring stiffness and damping) are derived either symbolically and/or numerically.
Dynamic Optimization of Multi-Spacecraft Relative Navigation Configurations in the Earth-Moon System
NASA Technical Reports Server (NTRS)
Villac, Benjamin; Chow, Channing; Lo, Martin; Hintz, Gerald; Nazari, Zahra
2010-01-01
In this paper, the notion of relative navigation introduced by Hill, Lo and Born is analyzed for a large class of periodic orbits in the Earth-Moon three-body problem, due to its potential in supporting Moon exploration efforts. In particular, a navigation metric is introduced and used as a cost function to optimize over a class of periodic orbits. While the problem could be solve locally as an optimal control problem, a dynamical based approach that allows for a global/systematic view of the problem is proposed. First, the simpler problem of multiple spacecraft placement on a given periodic orbit is solved before the notion of continuation and bifurcation analysis is used to expand the range of solutions thus obtained.
The salt marsh vegetation spread dynamics simulation and prediction based on conditions optimized CA
NASA Astrophysics Data System (ADS)
Guan, Yujuan; Zhang, Liquan
2006-10-01
The biodiversity conservation and management of the salt marsh vegetation relies on processing their spatial information. Nowadays, more attentions are focused on their classification surveying and describing qualitatively dynamics based on RS images interpreted, rather than on simulating and predicting their dynamics quantitatively, which is of greater importance for managing and planning the salt marsh vegetation. In this paper, our notion is to make a dynamic model on large-scale and to provide a virtual laboratory in which researchers can run it according requirements. Firstly, the characteristic of the cellular automata was analyzed and a conclusion indicated that it was necessary for a CA model to be extended geographically under varying conditions of space-time circumstance in order to make results matched the facts accurately. Based on the conventional cellular automata model, the author introduced several new conditions to optimize it for simulating the vegetation objectively, such as elevation, growth speed, invading ability, variation and inheriting and so on. Hence the CA cells and remote sensing image pixels, cell neighbors and pixel neighbors, cell rules and nature of the plants were unified respectively. Taking JiuDuanSha as the test site, where holds mainly Phragmites australis (P.australis) community, Scirpus mariqueter (S.mariqueter) community and Spartina alterniflora (S.alterniflora) community. The paper explored the process of making simulation and predictions about these salt marsh vegetable changing with the conditions optimized CA (COCA) model, and examined the links among data, statistical models, and ecological predictions. This study exploited the potential of applying Conditioned Optimized CA model technique to solve this problem.
The importance of functional form in optimal control solutions of problems in population dynamics
Runge, M.C.; Johnson, F.A.
2002-01-01
Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.
Integrated aerodynamic and dynamic optimization of tiltrotor wing and rotor systems
NASA Astrophysics Data System (ADS)
Orr, Stanley
Rotorcraft analysis and design must account for interdisciplinary interactions, especially between aerodynamics, structural, and dynamics responses. In this design domain, the work of disciplinary experts is segregated to a large extent. Furthermore, the design of subsystems is also segregated. In this environment it is difficult to account for interdisciplinary interactions and exploit the coupling between sub systems. This work examined the multidisciplinary nature of a tiltrotor aircraft focusing on the aerodynamic design of the rotor, and the structural and dynamic design of the rotor and wing systems. The design was considered to be in the preliminary design phase, after the basic configuration is set and before fine details of the design are defined. Attention was focused on developing an optimization problem formulation that was sufficiently complete so that optimization technologies and heuristic strategies could be developed and evaluated for tiltrotor design. Furthermore the level of detail in the analysis was consistent with the phase of design. Coupling between the aerodynamic, dynamic, and structural design was shown to be significant. Additionally, the coupling between the design of the wing and rotor systems was also significant, so that an integrated design was required. This work developed integrated design strategies for solving this design problem efficiently, while exploiting the couplings. Sum of system weights was the main objective along with vibratory rotor hub loads while hover performance, strength, frequency placement, and stability were constraints. Design variables included blade aerodynamic planform and twist as well as the details of composite D-spar blade sections, and details of composite torque-box wing sections. Cruise mode rotor speed and wing thickness were also included as design variables. A genetic algorithm based collaborative optimization was used as the solution framework for the global optimal search. The problem was decomposed between the wing and rotor systems. Response surface and neural network based approximations were used to represent the interactions between the temporarily decoupled systems. Surrogate models were also employed for the basic system responses. The approach was found to improve computational efficiency over the basic all-in-one optimization and the sequential design approach that is typical of industry practice.
NASA Astrophysics Data System (ADS)
Davidsen, Claus; Liu, Suxia; Mo, Xingguo; Rosbjerg, Dan; Bauer-Gottwein, Peter
2014-05-01
Optimal management of conjunctive use of surface water and groundwater has been attempted with different algorithms in the literature. In this study, a hydro-economic modelling approach to optimize conjunctive use of scarce surface water and groundwater resources under uncertainty is presented. A stochastic dynamic programming (SDP) approach is used to minimize the basin-wide total costs arising from water allocations and water curtailments. Dynamic allocation problems with inclusion of groundwater resources proved to be more complex to solve with SDP than pure surface water allocation problems due to head-dependent pumping costs. These dynamic pumping costs strongly affect the total costs and can lead to non-convexity of the future cost function. The water user groups (agriculture, industry, domestic) are characterized by inelastic demands and fixed water allocation and water supply curtailment costs. As in traditional SDP approaches, one step-ahead sub-problems are solved to find the optimal management at any time knowing the inflow scenario and reservoir/aquifer storage levels. These non-linear sub-problems are solved using a genetic algorithm (GA) that minimizes the sum of the immediate and future costs for given surface water reservoir and groundwater aquifer end storages. The immediate cost is found by solving a simple linear allocation sub-problem, and the future costs are assessed by interpolation in the total cost matrix from the following time step. Total costs for all stages, reservoir states, and inflow scenarios are used as future costs to drive a forward moving simulation under uncertain water availability. The use of a GA to solve the sub-problems is computationally more costly than a traditional SDP approach with linearly interpolated future costs. However, in a two-reservoir system the future cost function would have to be represented by a set of planes, and strict convexity in both the surface water and groundwater dimension cannot be maintained. The optimization framework based on the GA is still computationally feasible and represents a clean and customizable method. The method has been applied to the Ziya River basin, China. The basin is located on the North China Plain and is subject to severe water scarcity, which includes surface water droughts and groundwater over-pumping. The head-dependent groundwater pumping costs will enable assessment of the long-term effects of increased electricity prices on the groundwater pumping. The coupled optimization framework is used to assess realistic alternative development scenarios for the basin. In particular the potential for using electricity pricing policies to reach sustainable groundwater pumping is investigated.
Demonstration of Open Quantum System Optimal Control in Dynamic Nuclear Polarization
Sarah Sheldon; David G. Cory
2015-07-20
Dynamic nuclear polarization (DNP) is used in nuclear magnetic resonance (NMR) to transfer polarization from electron spins to nuclear spins. The resulting nuclear polarization enhancement can, in theory, be two or three orders of magnitude depending on the sample. In solid state systems, however, there are competing mechanisms of DNP, which, when occurring simultaneously, reduce the net polarization enhancement of the nuclear spin. We present a simple quantum description of DNP and apply optimal control theory (OCT) with an open quantum system framework to design pulses that select one DNP process and suppress the others. We demonstrate experimentally an order of magnitude improvement in the DNP enhancement using OCT pulses.
Cardoso, Rodrigo T N; da Cruz, André R; Wanner, Elizabeth F; Takahashi, Ricardo H C
2009-08-01
The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one. PMID:19267163
Riemannian geometric approach to human arm dynamics, movement optimization, and invariance
NASA Astrophysics Data System (ADS)
Biess, Armin; Flash, Tamar; Liebermann, Dario G.
2011-03-01
We present a generally covariant formulation of human arm dynamics and optimization principles in Riemannian configuration space. We extend the one-parameter family of mean-squared-derivative (MSD) cost functionals from Euclidean to Riemannian space, and we show that they are mathematically identical to the corresponding dynamic costs when formulated in a Riemannian space equipped with the kinetic energy metric. In particular, we derive the equivalence of the minimum-jerk and minimum-torque change models in this metric space. Solutions of the one-parameter family of MSD variational problems in Riemannian space are given by (reparametrized) geodesic paths, which correspond to movements with least muscular effort. Finally, movement invariants are derived from symmetries of the Riemannian manifold. We argue that the geometrical structure imposed on the arm’s configuration space may provide insights into the emerging properties of the movements generated by the motor system.
Optimization of stomatal conductance for maximum carbon gain under dynamic soil moisture
NASA Astrophysics Data System (ADS)
Manzoni, Stefano; Vico, Giulia; Palmroth, Sari; Porporato, Amilcare; Katul, Gabriel
2013-12-01
Optimization theories explain a variety of forms and functions in plants. At the leaf scale, it is often hypothesized that carbon gain is maximized, thus providing a quantifiable objective for a mathematical definition of optimality conditions. Eco-physiological trade-offs and limited resource availability introduce natural bounds to this optimization process. In particular, carbon uptake from the atmosphere is inherently linked to water losses from the soil as water is taken up by roots and evaporated. Hence, water availability in soils constrains the amount of carbon that can be taken up and assimilated into new biomass. The problem of maximizing photosynthesis at a given water availability by modifying stomatal conductance, the plant-controlled variable to be optimized, has been traditionally formulated for short time intervals over which soil moisture changes can be neglected. This simplification led to a mathematically open solution, where the undefined Lagrange multiplier of the optimization (equivalent to the marginal water use efficiency, ?) is then heuristically determined via data fitting. Here, a set of models based on different assumptions that account for soil moisture dynamics over an individual dry-down are proposed so as to provide closed analytical expressions for the carbon gain maximization problem. These novel solutions link the observed variability in ? over time, across soil moisture changes, and at different atmospheric CO2 concentrations to water use strategies ranging from intensive, in which all soil water is consumed by the end of the dry-down period, to more conservative, in which water stress is avoided by reducing transpiration.
Kumar, Navneet; Raj Chelliah, Thanga; Srivastava, S P
2015-07-01
Model Based Control (MBC) is one of the energy optimal controllers used in vector-controlled Induction Motor (IM) for controlling the excitation of motor in accordance with torque and speed. MBC offers energy conservation especially at part-load operation, but it creates ripples in torque and speed during load transition, leading to poor dynamic performance of the drive. This study investigates the opportunity for improving dynamic performance of a three-phase IM operating with MBC and proposes three control schemes: (i) MBC with a low pass filter (ii) torque producing current (iqs) injection in the output of speed controller (iii) Variable Structure Speed Controller (VSSC). The pre and post operation of MBC during load transition is also analyzed. The dynamic performance of a 1-hp, three-phase squirrel-cage IM with mine-hoist load diagram is tested. Test results are provided for the conventional field-oriented (constant flux) control and MBC (adjustable excitation) with proposed schemes. The effectiveness of proposed schemes is also illustrated for parametric variations. The test results and subsequent analysis confer that the motor dynamics improves significantly with all three proposed schemes in terms of overshoot/undershoot peak amplitude of torque and DC link power in addition to energy saving during load transitions. PMID:25820090
Network dynamics for optimal compressive-sensing input-signal recovery.
Barranca, Victor J; Kova?i?, Gregor; Zhou, Douglas; Cai, David
2014-10-01
By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs. PMID:25375568
Setrak J. Balian; Ren-Bao Liu; T. S. Monteiro
2015-04-22
There are two distinct techniques of proven effectiveness for extending the coherence lifetime of spin qubits in environments of other spins. One is dynamical decoupling, whereby the qubit is subjected to a carefully timed sequence of control pulses; the other is tuning the qubit towards 'optimal working points' (OWPs), which are sweet-spots for reduced decoherence in magnetic fields. By means of quantum many-body calculations, we investigate the effects of dynamical decoupling pulse sequences far from and near OWPs for a central donor qubit subject to decoherence from a nuclear spin bath. Key to understanding the behavior is to analyse the degree of suppression of the usually dominant contribution from independent pairs of flip-flopping spins within the many-body quantum bath. We find that to simulate recently measured Hahn echo decays at OWPs (lowest-order dynamical decoupling), one must consider clusters of three interacting spins, since independent pairs do not even give finite $T_2$ decay times. We show that while operating near OWPs, dynamical decoupling sequences require hundreds of pulses for a single order of magnitude enhancement of $T_2$, in contrast to regimes far from OWPs, where only about ten pulses are required.
Identification of extended Hammerstein systems using dynamic self-optimizing neural networks.
Ren, Xuemei; Lv, Xiaohua
2011-08-01
In this paper, a new dynamic self-optimizing neural network (DSONN) with online adjusting hidden layer and weights is proposed for a class of extended Hammerstein systems with non-Gaussian noises. Input vector to the network is first determined by means of system order estimation using a designated input signal. Then the hidden layer is generated online, which consists of a growing step according to the plant dynamics and a revised pruning step used to refine the hidden structure such that the generated model can be a minimal realization with satisfactory performance. The algorithm is capable of adjusting both the network structure and weights simultaneously by using of weight variations as the conditions of structure optimization. An integrated performance including the identification error and an additional entropy penalty term is employed such that the model can attenuate the non-Gaussian noises as well as match the unknown plant automatically with a suitable structure. Convergence of the weights is guaranteed by suitably choosing the learning rates. The proposed DSONN can be established without a priori knowledge of the unknown nonlinearity. The efficiency of the method is illustrated through the applications to three different Hammerstein systems. PMID:21708500
Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints
NASA Astrophysics Data System (ADS)
Cassandras, Christos G.; Zhuang, Shixin
2005-11-01
Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.
NASA Astrophysics Data System (ADS)
Nikulin, Vladimir V.
2004-01-01
Development of long-range laser communication devices capable of operating within Earth"s atmosphere is a challenging task. Turbulence effects cause changes of the refractive index along the propagation path that lead to phase distortions (aberrations), thus creating random optical energy redistribution in the spatial domain and imposing performance limitations on the laser systems. A common method of mitigating these effects suggests dynamic wavefront control. In this paper the proposed technique is based on correction of the distorted beam using an electrically addressed programmable spatial light modulator (SLM). The phase profile that we impose on the distorted laser beam is described by a general two-dimensional wavefront OPD function. The simplex algorithm facilitates dynamic optimization of the system performance metric J as a function of control parameters (the polynomial coefficients). The resultant polynomial is used for calculation of the corrective phase profiles to be loaded onto the programmable phase modulator. The proposed control algorithm is independent of the system model and offers the advantage of blind ("model-free") optimization. The wavefront correction system is implemented in a simulation setup. Its performance is assessed in terms of convergence, speed, robustness and the ability to reduce the effects of wavefront distortions.
On dynamically generating relevant elementary flux modes in a metabolic network using optimization.
Oddsdóttir, Hildur Æsa; Hagrot, Erika; Chotteau, Véronique; Forsgren, Anders
2015-10-01
Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution. PMID:25323319
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2013-01-01
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
Dynamic response and optimal design of curved metallic sandwich panels under blast loading.
Qi, Chang; Yang, Shu; Yang, Li-Jun; Han, Shou-Hong; Lu, Zhen-Hua
2014-01-01
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a "soft" outer face and a "hard" inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606
Dynamic Response and Optimal Design of Curved Metallic Sandwich Panels under Blast Loading
Yang, Shu; Han, Shou-Hong; Lu, Zhen-Hua
2014-01-01
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a “soft” outer face and a “hard” inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606
NASA Astrophysics Data System (ADS)
Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel
2011-01-01
Fundamental molecular selectivity limits are probed by exploiting laser-controlled quantum interferences for the creation of distinct spectral signatures in two flavin molecules, erstwhile nearly indistinguishable via steady-state methods. Optimal dynamic discrimination (ODD) uses optimally shaped laser fields to transiently amplify minute molecular variations that would otherwise go unnoticed with linear absorption and fluorescence techniques. ODD is experimentally demonstrated by combining an optimally shaped UV pump pulse with a time-delayed, fluorescence-depleting IR pulse for discrimination amongst riboflavin and flavin mononucleotide in aqueous solution, which are structurally and spectroscopically very similar. Closed-loop, adaptive pulse shaping discovers a set of UV pulses that induce disparate responses from the two flavins and allows for concomitant flavin discrimination of ˜16?. Additionally, attainment of ODD permits quantitative, analytical detection of the individual constituents in a flavin mixture. The successful implementation of ODD on quantum systems of such high complexity bodes well for the future development of the field and the use of ODD techniques in a variety of demanding practical applications.
Reduced-order model for dynamic optimization of pressure swing adsorption processes
Agarwal, A.; Biegler, L.; Zitney, S.
2007-01-01
Over the past decades, pressure swing adsorption (PSA) processes have been widely used as energy-efficient gas and liquid separation techniques, especially for high purity hydrogen purification from refinery gases. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either design or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approach to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. The study develops a reduced-order model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. Initially, a representative ensemble of solutions of the dynamic PDE system is constructed by solving a higher-order discretization of the model using the method of lines, a two-stage approach that discretizes the PDEs in space and then integrates the resulting DAEs over time. Next, the ROM method applies the Karhunen-Loeve expansion to derive a small set of empirical eigenfunctions (POD modes) which are used as basis functions within a Galerkin's projection framework to derive a low-order DAE system that accurately describes the dominant dynamics of the PDE system. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization before and making optimization problem computationally-efficient. The method has been applied to the dynamic coupled PDE-based model of a two-bed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The gas-phase mole fraction, solid-state loading and temperature profiles from the low-order ROM and from the high-order simulations have been compared. Moreover, the profiles for a different set of inputs and parameter values fed to the same ROM were compared with the accurate profiles from the high-order simulations. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes. Moreover, deviations from the ROM for different set of inputs and parameters suggest that a recalibration of the model is required for the optimization studies. Results for these will also be presented with the aforementioned results.
Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy
NASA Astrophysics Data System (ADS)
Smyth, Gregory; Bamber, Jeffrey C.; Evans, Philip M.; Bedford, James L.
2013-11-01
Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR-VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR-VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR-VMAT trajectory, was determined using Dijkstra’s algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50?Gy and V60?Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts.
Optimization of fluid front dynamics in porous media using rate control. I. Equal mobility fluids
NASA Astrophysics Data System (ADS)
Sudaryanto, Bagus; Yortsos, Yannis C.
2000-07-01
In applications involving the injection of a fluid in a porous medium to displace another fluid, a main objective is the maximization of the displacement efficiency. For a fixed arrangement of injection and production points (sources and sinks), such optimization is possible by controling the injection rate policy. Despite its practical relevance, however, this aspect has received scant attention in the literature. In this paper, we provide a fundamental approach based on optimal control theory, for the simplified case when the fluids are miscible, of equal viscosity, and in the absence of dispersion and gravity effects. Both homogeneous and heterogeneous porous media are considered. From a fluid dynamics viewpoint, this is a problem in the deformation of material lines in porous media, as a function of time-varying injection rates. It is shown that the optimal injection policy that maximizes the displacement efficiency, at the time of arrival of the injected fluid, is of the "bang-bang" type, in which the rates take their extreme values in the range allowed. This result applies to both homogeneous and heterogeneous media. Examples in simple geometries and for various constraints are shown, illustrating the efficiency improvement over the conventional approach of constant rate injection. In the heterogeneous case, the effect of the permeability heterogeneity, particularly its spatial correlation structure, on diverting the flow paths, is analyzed. It is shown that bang-bang injection remains the optimal approach, compared to constant rate, particularly if they were both designed under the assumption that the medium was homogeneous. Experiments in a homogeneous Hele-Shaw cell are found to be in good agreement with the theory.
NASA Astrophysics Data System (ADS)
Kollet, S. J.
2014-12-01
Theories of optimality and self-organization are appealing when dealing with non-linear systems, because based on first principles of thermodynamic these theories may lead to an intuitive interpretation and prediction of absolute values, directions, and interactions of gradients and fluxes, and universal inference laws for effective conductances. In this context, for example, the maximum entropy production principle received attention, because of its foundation in non-equilibrium thermodynamics, which appears to be useful in e.g., eco-hydrologic and atmospheric applications. A number of studies successfully applied this principle in the optimization of conductances in simplified and well-mixed open systems with external (periodic) forcing. In support-scale simulations of a variably saturated hillslope, the study presented here relaxes major simplifying assumptions by applying a realistic, arid atmospheric time series in spinup simulations to create a dynamic equilibrium utilizing the integrated hydrologic model ParFlow-CLM. The simulated hillslope exhibits time-varying internal circulation patterns due to the periodic atmospheric forcing, topography, and also heterogeneity by utilizing and optimizing all degrees of freedom provided by the soil-water retention relationship and free-moving water table. Because of the extreme non-linearity of variably saturated flow under arid climate conditions, the system is never well mixed and optimality principles relying on time-integrated gradients and fluxes do not appear to be applicable in the currently available theoretical framework. Here, integrated support-scale simulations may be useful in deriving novel theories for the application to real systems in future.
Dynamic Planar Convex Hull with Optimal Query Time and O(log n log log n) Update Time
Riko Jacob
Dynamic Planar Convex Hull with Optimal Query Time and O(log n #1; log log n) Update Time Gerth St supporting point inser- tions in amortized O(log n #1; log log log n) time, point deletions in amor- tized O(log n #1; log log n) time, and various queries about the convex hull in optimal O(log n) worst-case time
Vanregemorter, J.; Deconinck, F.
1984-01-01
The visual perception of temporal changes in a dynamic series of images, e.g. a recurrent movie display of an equilibrium gated angiography, depends strongly on the frequency of the changes in activity of the images. The human eye is three times as sensitive for frequencies at about ten hertz, as for frequencies lower than three hertz. This causes a low sensitivity for the relevant activity changes in the information of the series and a much higher sensitivity for activity changes due to noise. Linear combination of subsequent images allows to filter the frequency content of the series at about ten hertz. This does not affect the information of the series as this contains typically frequencies lower than three hertz (about the third harmonic in a typical angiography). For a filter which creates a new series of images by adding three subsequent images with factors a,b and a respectively, the optimal ratio A=a/b to reduce frequencies at ten hertz depends on the speed of projection of the dynamic series. A projection at 16 images per second leads to A=1.6. The effect of the filter is both objective on the static images (convolution smoothing) and subjective (psychophysical) on the dynamic series. It allows the reduction of the fast disturbing changes in the images due to noise and does not affect the diagnostic information in the images.
Optimal Variable Flip Angle Schemes For Dynamic Acquisition Of Exchanging Hyperpolarized Substrates
Xing, Yan; Reed, Galen D.; Pauly, John M.; Kerr, Adam B.; Larson, Peder E. Z.
2013-01-01
In metabolic MRI with hyperpolarized contrast agents, the signal levels vary over time due to T1 decay, T2 decay following RF excitations, and metabolic conversion. Efficient usage of the nonrenewable hyperpolarized magnetization requires specialized RF pulse schemes. In this work, we introduce two novel variable flip angle schemes for dynamic hyperpolarized MRI in which the flip angle is varied between excitations and between metabolites. These were optimized to distribute the magnetization relatively evenly throughout the acquisition by accounting for T1 decay, prior RF excitations, and metabolic conversion. Simulation results are presented to confirm the flip angle designs and evaluate the variability of signal dynamics across typical ranges of T1 and metabolic conversion. They were implemented using multiband spectral-spatial RF pulses to independently modulate the flip angle at various chemical shift frequencies. With these schemes we observed increased SNR of [1-13C]lactate generated from [1-13C]pyruvate, particularly at later time points. This will allow for improved characterization of tissue perfusion and metabolic profiles in dynamic hyperpolarized MRI. PMID:23845910
Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells
NASA Astrophysics Data System (ADS)
Spivey, Benjamin James
2011-07-01
Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.