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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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Stochastic Multiobjective Optimization: Sample Average Approximation and Applications
Journal of Optimization Theory and Applications, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fliege, Joerg, Xu, Huifu
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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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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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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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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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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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Sample Average Approximation For Functional Decisions Under Shape Constraints
2020 Winter Simulation Conference (WSC), 2020Sample average approximation methods are most often applied when the set of decision variables is finite. This research develops a method of finding optimal solutions to infinite-dimensional simulation optimization problems when the decision variable is a monotone function on a random variable used to model the uncertainty itself.
Dashi I. Singham, Henry Lam
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Sample Average Approximation Method for Compound Stochastic Optimization Problems
SIAM Journal on Optimization, 2013The paper studies stochastic optimization (programming) problems with compound functions containing expectations and extreme values of other random functions as arguments. Compound functions arise in various applications. A typical example is a variance function of nonlinear outcomes.
Ermoliev, Y.M., Norkin, V.
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Stochastic Variational Inequalities: Residual Minimization Smoothing Sample Average Approximations
SIAM Journal on Optimization, 2012The stochastic variational inequality (VI) has been used widely in engineering and economics as an effective mathematical model for a number of equilibrium problems involving uncertain data. This paper presents a new expected residual minimization (ERM) formulation for a class of stochastic VI.
Chen, X, Wets, RJ, Zhang, Y
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