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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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Book
28 Aug 1992
TL;DR: In this article, the authors considered the problem of optimal control of linear stochastic systems with partial information with an exponential-of-integral performance index (EoIPI).
Abstract: Preface 1. Linear filtering theory 2. Optimal stochastic control for linear dynamic systems with quadratic payoff 3. Optimal control of linear stochastic systems with an exponential-of-integral performance index 4. Non linear filtering theory 5. Perturbation methods in non linear filtering 6. Some explicit solutions of the Zakai equation 7. Some explicit controls for systems with partial observation 8. Stochastic maximum principle and dynamic programming for systems with partial observation 9. Existence results for stochastic control problems with partial information References Index.

514 citations

Journal ArticleDOI
TL;DR: By exploiting duality relations of convex analysis, the quantile model of stochastic dominance for general distributions is developed and it is shown that several models using quantiles and tail characteristics of the distribution are in harmony with the stoChastic dominance relation.
Abstract: We consider the problem of constructing mean-risk models which are consistent with the second degree stochastic dominance relation. By exploiting duality relations of convex analysis we develop the quantile model of stochastic dominance for general distributions. This allows us to show that several models using quantiles and tail characteristics of the distribution are in harmony with the stochastic dominance relation. We also provide stochastic linear programming formulations of these models.

510 citations

Journal ArticleDOI
TL;DR: The ITSP is applied to a hypothetical case study of water resources system operation and results indicate that reasonable solutions have been obtained and the information obtained can provide useful decision support for water managers.
Abstract: An inexact two-stage stochastic programming (ITSP) model is proposed for water resources management under uncertainty. The model is a hybrid of inexact optimization and two-stage stochastic programming. It can reflect not only uncertainties expressed as probability distributions but also those being available as intervals. The solution meth od for ITSP is computationally effective, which makes it applicable to practical problems. The ITSP is applied to a hypothetical case study of water resources system operation. The results indicate that reasonable solutions have been obtained. They are further analyzed and interpreted for generating decision alternatives and identifying significant factors that affect the system's performance. The information obtained through these post-optimality analyses can provide useful decision support for water managers.

501 citations

DOI
01 Jan 2007
TL;DR: This paper formulate minimal requirements that should be imposed on a scenario generation method before it can be used for solving the stochastic programming model and shows how the requirements can be tested.
Abstract: Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, however, normally comes in the form of continuous distributions or large data sets. Creating a limited discrete distribution from input is called scenario generation. In this paper, we discuss how to evaluate the quality or suitability of scenario generation methods for a given stochastic programming model. We formulate minimal requirements that should be imposed on a scenario generation method before it can be used for solving the stochastic programming model. We also show how the requirements can be tested. The procedures for testing a scenario generation method is illustrated on a case from portfolio management.

500 citations

Journal ArticleDOI
TL;DR: The bi-level linear case is addressed in detail and the reformulated optimization problem is linear save for a complementarity constraint of the form 〈u, g〉 = 0.

493 citations


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