- | ||||
|
![]()
|
A Comment on “Computational Complexity of Stochastic Programming Problems”
Grani A. Hanasusanto (grani.hanasusanto 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 (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. Keywords: stochastic programming; complexity theory Category 1: Stochastic Programming Citation: Download: [PDF] Entry Submitted: 03/16/2015 Modify/Update this entry | ||
Visitors | Authors | More about us | Links | |
Subscribe, Unsubscribe Digest Archive Search, Browse the Repository
|
Submit Update Policies |
Coordinator's Board Classification Scheme Credits Give us feedback |
Optimization Journals, Sites, Societies | |
![]() |