Convergence and Bound Computation for Chance Constrained Distributionally Robust Models using Sample Approximation

Fuente: arXiv
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Autori principali: Lei, Jiaqi, Mehrotra, Sanjay
Natura: Preprint
Pubblicazione: 2024
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author Lei, Jiaqi
Mehrotra, Sanjay
author_facet Lei, Jiaqi
Mehrotra, Sanjay
contents This paper considers a distributionally robust chance constraint model with a general ambiguity set. We show that a sample based approximation of this model converges under suitable sufficient conditions. We also show that upper and lower bounds on the optimal value of the model can be estimated statistically. Specific ambiguity sets are discussed as examples.
format Preprint
id arxiv_https___arxiv_org_abs_2408_12018
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Convergence and Bound Computation for Chance Constrained Distributionally Robust Models using Sample Approximation
Lei, Jiaqi
Mehrotra, Sanjay
Optimization and Control
This paper considers a distributionally robust chance constraint model with a general ambiguity set. We show that a sample based approximation of this model converges under suitable sufficient conditions. We also show that upper and lower bounds on the optimal value of the model can be estimated statistically. Specific ambiguity sets are discussed as examples.
title Convergence and Bound Computation for Chance Constrained Distributionally Robust Models using Sample Approximation
topic Optimization and Control
url https://arxiv.org/abs/2408.12018