Efficient Monte Carlo simulations for stochastic programming
نویسندگان
چکیده
This paper is concerned with chance constrained programming to deal with nonlinear optimization problems with random parameters. Specific Monte Carlo methods to evaluate the gradient and Hessian of probabilistic constraints are proposed and discussed. These methods are implemented in penalization optimization routines adapted to stochastic optimization. They are shown to reduce the computational cost of chance constrained programming substantially.
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تاریخ انتشار 2007