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The empirical behavior of sampling methods for stochastic programming · Study

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ArticleThe empirical behavior of sampling methods for stochastic programming

Jeff Linderoth2006

Article2006

The empirical behavior of sampling methods for stochastic programming

Jeff Linderoth · Alexander Shapiro · Stephen J. Wright

426 citations·0/0 files stored·Annals of Operations Research

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Risk and Portfolio Optimization Advanced Optimization Algorithms Research Simulation Techniques and Applications

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References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1987Large Sample Properties of Simulations Using Latin Hypercube Sampling 2002The Sample Average Approximation Method for Stochastic Discrete Optimization 1999Monte Carlo bounding techniques for determining solution quality in stochastic programs 1988Numerical Techniques for Stochastic Optimization 1991Stochastic Decomposition: An Algorithm for Two-Stage Linear Programs with Recourse

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2006On Complexity of Stochastic Programming Problems 2006Planning for Disruptions in Supply Chain Networks 2014Monte Carlo sampling-based methods for stochastic optimization 2009Light Robustness 2011Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion 2007Stochastic programming approach to optimization under uncertainty 2002The Sample Average Approximation Method for Stochastic Programs with Integer Recourse

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Details

Year2006

Typearticle

Published2006-02-01

VenueAnnals of Operations Research

ISSN-L0254-5330

Languageen

Citations426

Files0 public / 0

ShelfRisk and Portfolio Optimization

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doi10.1007/s10479-006-6169-8

openalex_workW2000257769

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2005

On complexity of multistage stochastic programs