A comment on "computational complexity of stochastic programming problems"

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Title: A comment on "computational complexity of stochastic programming problems"
Authors: Hanasusanto, GA
Kuhn, D
Wiesemann, W
Item Type: Journal Article
Abstract: Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Math Program A 106(3):423–432, 2006). Amongst others, that paper argues that linear two-stage stochastic programs with fixed recourse are #P-hard even if the random problem data is governed by independent uniform distributions. We show that Dyer and Stougie’s proof is not correct, and we offer a correction which establishes the stronger result that even the approximate solution of such problems is #P-hard for a sufficiently high accuracy. We also provide new results which indicate that linear two-stage stochastic programs with random recourse seem even more challenging to solve.
Issue Date: 22-Oct-2015
Date of Acceptance: 6-Oct-2015
URI: http://hdl.handle.net/10044/1/38437
DOI: https://dx.doi.org/10.1007/s10107-015-0958-2
ISSN: 0025-5610
Publisher: Springer Verlag
Start Page: 557
End Page: 569
Journal / Book Title: Mathematical Programming
Volume: 159
Issue: 1
Copyright Statement: © Springer Verlag 2015. The final publication is available at Springer via http://dx.doi.org/10.1007/s10107-015-0958-2
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/M028240/1
Keywords: Science & Technology
Physical Sciences
Computer Science, Software Engineering
Operations Research & Management Science
Mathematics, Applied
Computer Science
Stochastic programming
Complexity theory
Two-stage problems
Operations Research
0102 Applied Mathematics
0103 Numerical And Computational Mathematics
0802 Computation Theory And Mathematics
Publication Status: Published
Appears in Collections:Imperial College Business School

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