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Convergence Properties of Two-Stage Stochastic Programming
Journal of Optimization Theory and Applications, 2000The aim of the authors is to investigate a convergence rate of empirical estimates in the case of stochastic programming problems with mathematical expectation in the objective function and a ``deterministic'' constraint set. Of course, two-stage stochastic programming problems belong to this type of the problems. First, they recall a (rather complete)
Dai, L., Chen, C. H., Birge, J. R.
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Two-Stage Stochastic Programs with Mixed Probabilities
SIAM Journal on Optimization, 2007Summary: We extend the traditional two-stage linear stochastic program by probabilistic constraints imposed in the second stage. This adds nonlinearity such that basic arguments for analyzing the structure of linear two-stage stochastic programs have to be rethought from the very beginning.
Bosch, Paul +2 more
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Asymptotic Results of Stochastic Decomposition for Two-Stage Stochastic Quadratic Programming
SIAM Journal on Optimization, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Junyi Liu, Suvrajeet Sen
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Sequential Bounding Methods for Two-Stage Stochastic Programs
INFORMS Journal on Computing, 2016In rare situations, stochastic programs can be solved analytically. Otherwise, approximation is necessary to solve stochastic programs with a large or infinite number of scenarios to a desired level of accuracy. This involves statistical sampling or deterministic selection of a finite set of scenarios to obtain a tractable deterministic equivalent ...
Gose, Alexander H., Denton, Brian T.
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Two Stage Stochastic Linear Programs
1996Over the past several decades, linear programming (LP) has established itself as one of the most fundamental tools for planning. Its applications have become routine in several disciplines including those within engineering, business, economics, environmental studies and many others.
Julia L. Higle, Suvrajeet Sen
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Stochastic Decomposition for Two-Stage Stochastic Linear Programs with Random Cost Coefficients
INFORMS Journal on Computing, 2021Stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or ...
Harsha Gangammanavar +2 more
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Programmed Control of Two-Stage Stochastic Production Systems
Automation and Remote Control, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Exponential convergence of two-stage stochastic programming
IFAC Proceedings Volumes, 1999Abstract This paper considers a procedure of two-stage stochastic programming in which the performance function to be optimized is replaced by its empirical mean. This procedure converts a stochastic optimization problem into a deterministic one for which many methods are available.
Liyi Dai, Chun-Hung Chen, John Birge
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Two-Stage Stochastic Programming Problems
1995In this chapter we consider stochastic programming problems where decisions are made in two stages and the observation of a (vector valued) random variable takes place in between. Such problems are called two-stage stochastic programming problems or stochastic programming with recourse.
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Deviation Measures in Linear Two-Stage Stochastic Programming
Mathematical Methods of Operations Research, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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