Hanasusanto, Grani AdiwenaKuhn, DanielWiesemann, Wolfram2015-03-162015-03-162015-03-16201610.1007/s10107-015-0958-2https://infoscience.epfl.ch/handle/20.500.14299/112537WOS:000382053900018Although 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.Stochastic programmingComplexity theorytext::journal::journal article::research article