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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


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Journal ArticleDOI
TL;DR: An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front and a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm.

348 citations

Journal ArticleDOI
TL;DR: Valid inequalities and range contraction techniques that can be used to reduce the size of the search space of global optimization problems are presented and incorporated within the branch-and-bound framework to result in a branch- and-reduce global optimization algorithm.
Abstract: This paper presents valid inequalities and range contraction techniques that can be used to reduce the size of the search space of global optimization problems. To demonstrate the algorithmic usefulness of these techniques, we incorporate them within the branch-and-bound framework. This results in a branch-and-reduce global optimization algorithm. A detailed discussion of the algorithm components and theoretical properties are provided. Specialized algorithms for polynomial and multiplicative programs are developed. Extensive computational results are presented for engineering design problems, standard global optimization test problems, univariate polynomial programs, linear multiplicative programs, mixed-integer nonlinear programs and concave quadratic programs. For the problems solved, the computer implementation of the proposed algorithm provides very accurate solutions in modest computational time.

343 citations

Journal ArticleDOI
TL;DR: The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap using the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states.
Abstract: This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.

342 citations

01 Sep 1989
TL;DR: It is shown how a TD METHOD can beunderstood as a NOVEL SYNTHESIS of CONCEPTS from the theORY of STOCHASTIC DYNAMIC PROGRAMMING, which is the standard method for solving decision-making problems in binary systems.
Abstract: IN THIS REPORT WE SHOW HOW THE CLASS OF ADAPTIVE PREDICTION METHODS THAT SUTTON CALLED "TEMPORAL DIFFERENCE", OR TD, METHODS ARE RELATED TO THE THE- ORY OF SEQUENTIAL DECISION MAKING. TD METHODS HAVE BEEN USED AS "ADAPTIVE CRITICS" IN CONNECTIONIST LEARNING SYSTEMS,AND HAVE BEEN PROPOSED AS MODELS OF ANIMAL LEARNING IN CLASSICAL CONDITIONING EXPERIMENTS. HERE WE RELATE TD METHODS TO DECISION TASKS FORMULATED IN TERMS OF A STOCHASTIC DYNAMICAL SYSTEM WHOSE BEHAVIOR UNFOLDS OVER TIME UNDER THE INFLUENCE OF A DECISION MAKER''S ACTIONS. STRATEGIES ARE SOUGHT FOR SELECTING ACTIONS SO AS TO MAXI- MIZE A MEASURE OF LONG-TERM PAYOFF GAIN. MATHEMATICALLY, TASKS SUCH AS THIS CAN BE FORMULATED AS MARKOVIAN DECISION PROBLEMS, AND NUMEROUS METHODS HAVE BEEN PROPOSED FOR LEARNING HOW TO SOLVE SUCH PROBLEMS. WE SHOW HOW A TD METHOD CAN BE UNDERSTOOD AS A NOVEL SYNTHESIS OF CONCEPTS FROM THE THEORY OF STOCHASTIC DYNAMIC PROGRAMMING, WHICH COMPRISES THE STANDARD METHOD FOR SOLVING SUCH TASKS WHEN A MODEL OF THE DYNAMICAL SYSTEM IS AVAILABLE, AND THE THEORY OF PARAMETER ESTIMATION, WHICH PROVIDES THE APPROPRIATE CONTEXT FOR STUDYING LEARNING RULES IN THE FORM OF EQUATIONS FOR UPDATING ASSOCIA- TIVE STRENGTHS IN BEHAVIORAL MODELS, OR CONNECTION WEIGHTS IN CONNECTIONIST NETWORKS. BECAUSE THIS REPORT IS ORIENTED PRIMARILY TOWARD THE NON-ENGINEER INTERESTED IN ANIMAL LEARNING, IT PRESENTS TUTORIALS ON STOCHASTIC SEQUEN- TIAL DECISION TASKS, STOCHASTIC DYNAMIC PROGRAMMING, AND PARAMETER ESTIMATI

342 citations

Journal ArticleDOI
TL;DR: A stochastic branch and bound method for solving Stochastic global optimization problems is proposed and random accuracy estimates derived.
Abstract: A stochastic branch and bound method for solving stochastic global optimization problems is proposed. As in the deterministic case, the feasible set is partitioned into compact subsets. To guide the partitioning process the method uses stochastic upper and lower estimates of the optimal value of the objective function in each subset. Convergence of the method is proved and random accuracy estimates derived. Methods for constructing stochastic upper and lower bounds are discussed. The theoretical considerations are illustrated with an example of a facility location problem.

340 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532