Nonlinear Stochastic Optimization by the Monte-Carlo Method

Article type: Research Article

Authors: Sakalauskas, Leonidas

Affiliations: Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: sakal@ktl.mii.lt

Abstract: Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem from acceptable volume of Monte-Carlo trials. The examples of application of the developed method to importance sampling and the Weber location problem are also considered.

Keywords: Monte-Carlo method, stochastic optimization, statistical decisions

DOI: 10.3233/INF-2000-11409

Journal: Informatica, vol. 11, no. 4, pp. 455-468, 2000