A Comment on “Computational Complexity of Stochastic Programming Problems”

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 (Mathematical Programming 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 prove that the approximate solution of linear two-stage stochastic programs with random recourse is strongly #P-hard.


Published in:
Mathematical Programming, 159, 1, 557-569
Year:
2016
Publisher:
Heidelberg, Springer Heidelberg
ISSN:
0025-5610
Keywords:
Note:
Available from Optimization Online
Laboratories:




 Record created 2015-03-16, last modified 2018-09-13

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