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Main Authors: Tian, Lai, Royset, Johannes O.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.15801
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author Tian, Lai
Royset, Johannes O.
author_facet Tian, Lai
Royset, Johannes O.
contents In this paper, we show how approximating Rockafellians serve as a principled and effective alternative for improving the stability of stochastic programs under distributional changes. Unlike previous efforts that focus on special distributions and continuous integrands, our results accommodate general probability distributions and discontinuous integrands. Thus, our results apply to chance-constrained programs, for which we obtain improved qualitative and quantitative stability results under weaker assumptions pertaining to metric subregularity and upper outer-Minkowski content.
format Preprint
id arxiv_https___arxiv_org_abs_2507_15801
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Approximating Rockafellians Mitigate Distributional Perturbations: Discontinuous Integrands and Chance-Constrained Applications
Tian, Lai
Royset, Johannes O.
Optimization and Control
In this paper, we show how approximating Rockafellians serve as a principled and effective alternative for improving the stability of stochastic programs under distributional changes. Unlike previous efforts that focus on special distributions and continuous integrands, our results accommodate general probability distributions and discontinuous integrands. Thus, our results apply to chance-constrained programs, for which we obtain improved qualitative and quantitative stability results under weaker assumptions pertaining to metric subregularity and upper outer-Minkowski content.
title Approximating Rockafellians Mitigate Distributional Perturbations: Discontinuous Integrands and Chance-Constrained Applications
topic Optimization and Control
url https://arxiv.org/abs/2507.15801