Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation
Xuecheng Tian,
Bo Jiang,
King-Wah Pang
et al.
Abstract:Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contex… Show more
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