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Moderate Deviations and Invariance Principles for Sample Average Approximations
SIAM Journal on Optimization, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gao, M, Yiu, KFC
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Logarithmic sample bounds for Sample Average Approximation with capacity- or budget-constraints
Operations Research Letters, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Caleb Bugg, Anil Aswani
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Optimal Budget Allocation for Sample Average Approximation
Operations Research, 2013The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem.
Royset, Johannes O., Szechtman, Roberto
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Sample average approximation methods for stochastic MINLPs
Computers & Chemical Engineering, 2004One approach to process design with uncertain parameters is to formulate a stochastic MINLP. When there are many uncertain parameters, the number of samples becomes unmanageably large and computing the solution to the MINLP can be difficult and very time consuming.
Jing Wei, Matthew J. Realff
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Stochastic Multiobjective Optimization: Sample Average Approximation and Applications
Journal of Optimization Theory and Applications, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jörg Fliege, Huifu Xu
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Sample average approximations in optimal control of uncertain systems
52nd IEEE Conference on Decision and Control, 2013This paper focuses on an optimal control problem in which the objective is to minimize the expectation of a cost functional with stochastic parameters. The inclusion of the stochastic parameters in the objective raises new theoretical and computational challenges not present in a standard nonlinear optimal control problem.
Royset, Johannes O. +2 more
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A Guide to Sample Average Approximation
2014This chapter reviews the principles of sample average approximation (SAA) for solving simulation optimization problems. We provide an accessible overview of the area and survey interesting recent developments. We explain when one might want to use SAA and when one might expect it to provide good-quality solutions.
Sujin Kim +2 more
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Enhancing the sample average approximation method with U designs
Biometrika, 2010Summary: Many computational problems in statistics can be cast as stochastic programs that are optimization problems whose objective functions are multi-dimensional integrals. The sample average approximation method is widely used for solving such a problem, which first constructs a sampling-based approximation to the objective function and then finds ...
Qi Tang, Peter Z. G. Qian
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Acceleration on Adaptive Importance Sampling with Sample Average Approximation
SIAM Journal on Scientific Computing, 2017Summary: We construct and analyze acceleration techniques for adaptive Monte Carlo simulations for general multivariate probability laws when the sample average approximation is employed for optimal parameter search. Our goal is to accelerate the adaptive Monte Carlo estimation by leading the parameter search line based on the sample average ...
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