Implementable Algorithm for Stochastic Optimization Using Sample Average Approximations
Abstract
We develop an implementable algorithm for stochastic
optimization problems involving probability functions. Such problems
arise in the design of structural and mechanical systems. The algorithm
consists of a nonlinear optimization algorithm applied to sample
average approximations and a precision-adjustment rule. The sample
average approximations are constructed using Monte Carlo simulations
or importance sampling techniques. We prove that the algorithm
converges to a solution with probability one and illustrate its use by
an example involving a reliability-based optimal design.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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