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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.


Papers
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Journal ArticleDOI
TL;DR: This paper comprehensively discusses about the effectiveness of incorporating a risk measure in a two-stage stochastic model and proves the capabilities and acceptability of the developed risk-averse approach and the affects of risk parameters in the model behavior.

150 citations

Journal ArticleDOI
TL;DR: In this article, a parametric programming approach is proposed for the analysis of linear process engineering problems under uncertainty, and a novel branch and bound algorithm is presented for the solut....
Abstract: In this paper, a parametric programming approach is proposed for the analysis of linear process engineering problems under uncertainty. A novel branch and bound algorithm is presented for the solut...

150 citations

Proceedings ArticleDOI
07 Nov 2013
TL;DR: A new and rigorous SC correlation (SCC) measure is introduced, and it is shown that, contrary to intuition, correlation can be exploited as a resource in SC design and can be significantly more efficient and more accurate than traditional SC circuits.
Abstract: Stochastic computing (SC) is a re-emerging computing paradigm which enables ultra-low power and massive parallelism in important applications like real-time image processing. It is characterized by its use of pseudo-random numbers implemented by 0-1 sequences called stochastic numbers (SNs) and interpreted as probabilities. Accuracy is usually assumed to depend on the interacting SNs being highly independent or uncorrelated in a loosely specified way. This paper introduces a new and rigorous SC correlation (SCC) measure for SNs, and shows that, contrary to intuition, correlation can be exploited as a resource in SC design. We propose a general framework for analyzing and designing combinational circuits with correlated inputs, and demonstrate that such circuits can be significantly more efficient and more accurate than traditional SC circuits. We also provide a method of analyzing stochastic sequential circuits, which tend to have inherently correlated state variables and have proven very hard to analyze.

150 citations

Journal ArticleDOI
TL;DR: It is shown that convergence properties of the decomposition method are heavily dependent on sparsity of the linking constraints and application to large-scale linear programming and stochastic programming is discussed.
Abstract: A decomposition method for large-scale convex optimization problems with block-angular structure and many linking constraints is analysed. The method is based on a separable approximation of the augmented Lagrangian function. Weak global convergence of the method is proved and speed of convergence analysed. It is shown that convergence properties of the method are heavily dependent on sparsity of the linking constraints. Application to large-scale linear programming and stochastic programming is discussed.

150 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provided sufficient conditions for the equilibrium price system and a vector of exogenously specified state variable processes to form a diffusion process in a pure exchange economy, which involves smoothness of agents' utility functions and certain nice properties of the aggregate endowment process and the dividend processes of traded assets.
Abstract: This paper provides sufficient conditions for the equilibrium price system and a vector of exogenously specified state variable processes to form a diffusion process in a pure exchange economy. The conditions involve smoothness of agents' utility functions and certain nice properties of the aggregate endowment process and the dividend processes of traded assets. In place of the dynamic programming, a martingale representation technique is utilized to characterize equilibrium portfolio policies. This technique is useful even when there does not exist a finite dimensional Markov structure in the economy and thus the Markovian stochastic dynamic programming is not applicable. A gents are shown to hold certain hedging mutual funds and the riskless asset. In contrast to earlier results, the market portfolio does not have a special role in hedging, since the markets are dynamically complete. When there exists a finite dimensional Markov system in the economy, the dimension of the hedging demand identified through the Markovian dynamic programming may be much larger than that identified by the martingale method. Copyright 1987 by The Econometric Society.

150 citations


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