Topic
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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Papers
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TL;DR: By taking advantage of the loosely coupled structure, it is shown that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.
Abstract: Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result in very large scale combinatorial optimization problems. In this paper, we report on the solution of some of the largest stochastic combinatorial optimization problems consisting of over a million binary variables. While the methodology is quite general, the specific application with which we conduct our experiments arises in stochastic server location problems. The main observation is that stochastic combinatorial optimization problems are comprised of loosely coupled subsystems. By taking advantage of the loosely coupled structure, we show that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.
98 citations
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TL;DR: The relationship between fuzzy programming and stochastic programming is determined and equivalent precise analogues are derived for these problems.
97 citations
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TL;DR: It is shown that conventional optimization strategies are bound to outperform stochastic methods in the long run, and an optimization strategy that combines the benefits of both conventional and Stochastic optimization is reviewed.
Abstract: Randomized source‐encoding has recently been proposed as a way to dramatically reduce the costs of full waveform inversion. The main idea is to replace all sequential sources by a small number of simultaneous sources. This introduces random cross‐talk in model updates and special stochastic optimization strategies are required to deal with this. Two problems arise with this approach: i) source‐encoding can only be applied to fixed‐spread acquisition setups and ii) stochastic optimization methods tend to converge very slowly, relying on averaging to suppress the cross‐talk. Although the slow convergence is partly off‐set by a low iteration cost, we show that conventional optimization strategies are bound to outperform stochastic methods in the long run. In this paper we argue that we do not need randomized source‐encoding to reap the benefits of stochastic optimization and we review an optimization strategy that combines the benefits of both conventional and stochastic optimization. The method uses a gradually increasing batch of sources. Thus, iterations are initially very cheap and this allows the method to make fast progress in the beginning. As the batch‐size grows, the method behaves like conventional optimization, allowing for fast convergence. Stylized numerical examples suggest that the stochastic and hybrid methods perform equally well with and without source‐encoding and that the hybrid method outperforms both conventional and stochastic optimization. The method does not rely on source‐encoding techniques and can thus be applied to marine data. We illustrate this on a realistic synthetic model.
97 citations
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TL;DR: Fuzzy Linear Programming (FLP) is realized that FLP is an easy and flexible approach that can be a serious competitor for other confronting uncertainties approaches, i.e. stochastic and Minimax Regret strategies.
97 citations
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TL;DR: This paper studies the performance of two stochastic local search algorithms for the biobjective Quadratic Assignment Problem with different degrees of correlation between the flow matrices to suggest that the performance with respect to solution quality and computation time depends strongly on the correlation between those matrices.
97 citations