|edoc-Server der Humboldt-Universität zu Berlin|
Alexander Shapiro, Georgia Institute of Technology, Atlanta; Ohio State University|
Tito Homem-de-Mello, Georgia Institute of Technology, Atlanta; Ohio State University
|Title:||On Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs|
|Date of Acceptance:||18.10.1999|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Keywords (eng):||Monte Carlo simulation, Two-stage stochastic programming with recourse, Large Deviations theory, convex analysis|
SIAM Journal on Optimization 1.2000 (Vol. 11)
Society for Industrial and Applied Mathematics - SIAM (Philadelphia, Pa.)
|Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.||Endnote Bibtex|
|In this paper we discuss Monte Carlo simulation based approximations of a stochastic programming problem. We show that if the corresponding random functions are convex piecewise smooth and the distribution is discrete, then (under mild additional assumptions) an opitmal solution of the approximating problem provides an exact optimal solution of the true problem with probability one for sufficiently large sample size. Moreover, by using theory of Large Deviations, we show that the probability of such an event approaches one exponentially fast with increase of the sample size. In particular, this happens in the case of two stage stochastic programming with recourse if the corresponding distributions are discrete. The obtained results suggest that, in such cases, Monte Carlo simulation based methods could be very efficient. We present some numerical examples to illustrate the involved ideas.|
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