Nonlinear Stochastic Optimization by the Monte-Carlo Method
نویسنده
چکیده
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.
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عنوان ژورنال:
- Informatica, Lith. Acad. Sci.
دوره 11 شماره
صفحات -
تاریخ انتشار 2000