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Heteroscedasticity-aware residuals-based contextual stochastic optimization

Rohit Kannan(rohitk***at***alum.mit.edu)
Guzin Bayraksan(bayraksan.1***at***osu.edu)
James Luedtke(jim.luedtke***at***wisc.edu)

Abstract: We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual stochastic optimization that can adapt to heteroscedasticity. We identify conditions on the stochastic program, data generation process, and the prediction setup under which these generalizations possess asymptotic and finite sample guarantees for a class of stochastic programs, including two-stage stochastic mixed-integer programs with continuous recourse. We verify that our assumptions hold for popular parametric and nonparametric regression methods.

Keywords: Data-driven stochastic programming, distributionally robust optimization, covariates, regression, heteroscedasticity, convergence rate, large deviations

Category 1: Stochastic Programming

Category 2: Robust Optimization

Citation:

Download: [PDF]

Entry Submitted: 01/08/2021
Entry Accepted: 01/08/2021
Entry Last Modified: 01/08/2021

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