Optimal surveillance against foot-and-mouth disease: A sample average approximation approach. [PDF]
PLoS ONE, 2020 Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty.
Tom Kompas +5 more
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Smoothing sample average approximation method for solving stochastic second-order-cone complementarity problems [PDF]
Journal of Inequalities and Applications, 2018 In this paper, we consider stochastic second-order-cone complementarity problems (SSOCCP). We first use the so-called second-order-cone complementarity function to present an expected residual minimization (ERM) model for giving reasonable solutions of ...
Meiju Luo, Yan Zhang
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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, Xin Chen, Niao He
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The correction of Inelastic Neutron Scattering data of organic samples using the Average Functional Group Approximation [PDF]
EPJ Web of Conferences, 2022 The use of the Average Functional Group Approximation for self-shielding corrections at inelastic neutron spectrometers is discussed. By taking triptindane as a case study, we use the above-mentioned approximation to simulate a synthetic dynamic ...
Preziosi Enrico +3 more
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Sample average approximation for risk-averse problems: A virtual power plant scheduling application
EURO Journal on Computational Optimization, 2021 In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices.
Ricardo M. Lima +5 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 Anderson, Andy Philpott
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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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Deterministic Bi-Criteria Model for Solving Stochastic Mixed Vector Variational Inequality Problems
Mathematics, 2023 In this paper, we consider stochastic mixed vector variational inequality problems. Firstly, we present an equivalent form for the stochastic mixed vector variational inequality problems.
Meiju Luo, Menghan Du, Yue Zhang
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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 ...
Jaiswal, Prateek +2 more
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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 Approxima...
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