Sensitivity method for basis inverse representation in multistage stochastic linear programming problems

J. Gondzio; A. Ruszczyński

doi:10.1007/BF00940892

Sensitivity method for basis inverse representation in multistage stochastic linear programming problems

J. Gondzio^*, A. Ruszczyński

^*Corresponding author for this work

School of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

A version of the simplex method for solving stochastic linear control problems is presented. The method uses a compact basis inverse representation that extensively exploits the original problem data and takes advantage of the supersparse structure of the problem. Computational experience indicates that the method is capable of solving large problems.

Original language	English
Pages (from-to)	221-242
Number of pages	22
Journal	Journal of Optimization Theory and Applications
Volume	74
Issue number	2
DOIs	https://doi.org/10.1007/BF00940892
Publication status	Published - 1 Aug 1992

Keywords / Materials (for Non-textual outputs)

Linear programming
simplex method
stochastic programming

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10.1007/BF00940892

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@article{be073d66e1de463ea2124b8f283b814b,

title = "Sensitivity method for basis inverse representation in multistage stochastic linear programming problems",

abstract = "A version of the simplex method for solving stochastic linear control problems is presented. The method uses a compact basis inverse representation that extensively exploits the original problem data and takes advantage of the supersparse structure of the problem. Computational experience indicates that the method is capable of solving large problems.",

keywords = "Linear programming, simplex method, stochastic programming",

author = "J. Gondzio and A. Ruszczy{\'n}ski",

year = "1992",

month = aug,

day = "1",

doi = "10.1007/BF00940892",

language = "English",

volume = "74",

pages = "221--242",

journal = "Journal of Optimization Theory and Applications",

issn = "0022-3239",

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T1 - Sensitivity method for basis inverse representation in multistage stochastic linear programming problems

AU - Gondzio, J.

AU - Ruszczyński, A.

PY - 1992/8/1

Y1 - 1992/8/1

N2 - A version of the simplex method for solving stochastic linear control problems is presented. The method uses a compact basis inverse representation that extensively exploits the original problem data and takes advantage of the supersparse structure of the problem. Computational experience indicates that the method is capable of solving large problems.

AB - A version of the simplex method for solving stochastic linear control problems is presented. The method uses a compact basis inverse representation that extensively exploits the original problem data and takes advantage of the supersparse structure of the problem. Computational experience indicates that the method is capable of solving large problems.

KW - Linear programming

KW - simplex method

KW - stochastic programming

UR - http://www.scopus.com/inward/record.url?scp=0026910259&partnerID=8YFLogxK

U2 - 10.1007/BF00940892

DO - 10.1007/BF00940892

M3 - Article

AN - SCOPUS:0026910259

SN - 0022-3239

VL - 74

SP - 221

EP - 242

JO - Journal of Optimization Theory and Applications

JF - Journal of Optimization Theory and Applications

IS - 2

ER -

Sensitivity method for basis inverse representation in multistage stochastic linear programming problems

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

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