Nonlinear Stochastic Optimization by the Monte-Carlo Method

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. Key words: Monte-Carlo method, stochastic optimization, statistical decisions.

• Type: Journal
• Year: 2000
• Where: INFORMATICALT
• Authors: Leonidas Sakalauskas
• Keywords: INFORMATICALT 2000, Monte-Carlo, Monte-Carlo Sample Size, Stochastic Optimization Problem