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Testing successive regression approximations by large-scale two-stage problems

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  • István Deák

Abstract

A heuristic procedure, called successive regression approximations (SRA) has been developed for solving stochastic programming problems. They range from equation solving to probabilistic constrained and two-stage models through a combined model of Prékopa. We show here, that due to enhancements in the computer program, SRA can be used to solve large-scale two-stage problems with 100 first stage decision variables and a 120 dimensional normally distributed random right hand side vector in the second stage problem. A FORTRAN source program and computational results for 124 problems are presented at www.uni-corvinus.hu/~ideak1 . Copyright Springer Science+Business Media, LLC 2011

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  • István Deák, 2011. "Testing successive regression approximations by large-scale two-stage problems," Annals of Operations Research, Springer, vol. 186(1), pages 83-99, June.
    • Handle: RePEc:spr:annopr:v:186:y:2011:i:1:p:83-99:10.1007/s10479-009-0602-8
    DOI: 10.1007/s10479-009-0602-8
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    1. John M. Mulvey & Andrzej Ruszczyński, 1995. "A New Scenario Decomposition Method for Large-Scale Stochastic Optimization," Operations Research, INFORMS, vol. 43(3), pages 477-490, June.
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