Implementable Algorithm for Stochastic Optimization Using Sample Average Approximations

J. O. Royset and E. Polak
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J. O. Royset: US Naval Postgraduate School
E. Polak: University of California

Journal of Optimization Theory and Applications, 2004, vol. 122, issue 1, No 7, 157-184

Abstract: 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.

Keywords: Stochastic optimization; sample average approximations; Monte Carlo simulations; reliability-based optimal designs (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1023/B:JOTA.0000041734.06199.71

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