Results 11 to 20 of about 186,684 (266)
Robust sample average approximation [PDF]
Mathematical Programming, 2017 Sample average approximation (SAA) is a widely popular approach to data-driven decision-making under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong asymptotic performance guarantees. Similar guarantees, however, do not typically hold in finite samples.
Dimitris Bertsimas +2 more
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OPTIMAL ALLOCATIONS FOR SAMPLE AVERAGE APPROXIMATION [PDF]
2018 Winter Simulation Conference (WSC), 2018 We consider a single stage stochastic program without recourse with a strictly convex loss function. We assume a compact decision space and grid it with a finite set of points. In addition, we assume that the decision maker can generate samples of the stochastic variable independently at each grid point and form a sample average approximation (SAA) of ...
Prateek Jaiswal +2 more
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Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization [PDF]
SIAM Journal on Optimization, 2020 Typo corrected. Reference added.
Yifan Hu 0020, Xin Chen 0026, Niao He
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Stochastic approximation versus sample average approximation for Wasserstein barycenters [PDF]
Optimization Methods and Software, 2021 In the machine learning and optimization community, there are two main approaches for the convex risk minimization problem, namely the Stochastic Approximation (SA) and the Sample Average Approximation (SAA). In terms of the oracle complexity (required number of stochastic gradient evaluations), both approaches are considered equivalent on average (up ...
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On Feasibility of Sample Average Approximation Solutions [PDF]
SIAM Journal on Optimization, 2020 When there are infinitely many scenarios, the current studies of two-stage stochastic programming problems rely on the relatively complete recourse assumption. However, such assumption can be unrealistic for many real-world problems. This motivates us to study general stochastic programming problems where the sample average approximation (SAA ...
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Sample Average Approximation for Black-Box VI
CoRR, 2023 We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes. Our approach involves using a sequence of sample average approximation (SAA) problems. SAA approximates the solution of stochastic optimization problems by transforming them into deterministic ones.
Javier Burroni +2 more
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Improving Sample Average Approximation Using Distributional Robustness
INFORMS Journal on Optimization, 2022 Sample average approximation is a popular approach to solving stochastic optimization problems. It has been widely observed that some form of robustification of these problems often improves the out-of-sample performance of the solution estimators. In estimation problems, this improvement boils down to a trade-off between the opposing effects of bias ...
Edward J. Anderson, Andy Philpott
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The Sample Average Approximation Method for Stochastic Discrete Optimization [PDF]
SIAM Journal on Optimization, 2002 The authors study a Monte Carlo simulation-based approach to stochastic discrete optimization problems of the form \(\min_{x\in S}\{g(x):= E_PG(x, W)\}\), where \(W\) is a random vector having probability distribution \(P\), \(S\) is a finite set, \(G(x,w)\) is a real-valued function of two (vector) variables \(x\) and \(w\), and \(E_PG(x, W)= \int G(x,
Anton J. Kleywegt +2 more
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Asymptotic Analysis for One-Stage Stochastic Linear Complementarity Problems and Applications
Mathematics, 2023 One-stage stochastic linear complementarity problem (SLCP) is a special case of a multi-stage stochastic linear complementarity problem, which has important applications in economic engineering and operations management.
Shuang Lin, Jie Zhang, Chen Qiu
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Sample average approximation of stochastic dominance constrained programs [PDF]
Mathematical Programming, 2010 The paper develops an algorithm for the solution of optimization problems with multi-variate second-order stochastic dominance constraints. The sample Average Approximation (SAA) method is applied to a relaxation of this problem. For the resulting semi-infinite program, asymptotic convergence of optimal values and optimal solutions (as the sample size ...
Jian Hu +2 more
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