Please use this identifier to cite or link to this item: https://doi.org/10.1023/B:JOGO.0000015311.63755.01
Title: A new decomposition technique in solving multistage stochastic linear programs by infeasible interior point methods
Authors: Liu, X. 
Sun, J. 
Keywords: Decomposition
Infeasible primal-dual interior point method
Scenario analysis
Stochastic linear programs
Issue Date: 2004
Source: Liu, X., Sun, J. (2004). A new decomposition technique in solving multistage stochastic linear programs by infeasible interior point methods. Journal of Global Optimization 28 (2) : 197-215. ScholarBank@NUS Repository. https://doi.org/10.1023/B:JOGO.0000015311.63755.01
Abstract: Multistage stochastic linear programming (MSLP) is a powerful tool for making decisions under uncertainty. A deterministic equivalent problem of MSLP is a large-scale linear program with nonanticipativity constraints. Recently developed infeasible interior point methods are used to solve the resulting linear program. Technical problems arising from this approach include rank reduction and computation of search directions. The sparsity of the nonanticipativity constraints and the special structure of the problem are exploited by the interior point method. Preliminary numerical results are reported. The study shows that, by combining the infeasible interior point methods and specific decomposition techniques, it is possible to greatly improve the computability of multistage stochastic linear programs.
Source Title: Journal of Global Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/44230
ISSN: 09255001
DOI: 10.1023/B:JOGO.0000015311.63755.01
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