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<p>In this paper, we consider the convergence analysis of data-driven mathematical programs with distributionally robust chance constraints (MPDRCC) without continuity condition of distributionally robust probability functions. Combining with the data-driven approximation, we also propose a difference of convex (DC) approximation method to MPDRCC. We give the convergence analysis of the DC approximation method without continuity condition of distributionally robust probability functions and apply a recent DC algorithm to solve them. In the numerical examples, the convergence trends of data-driven MPDRCC and data-driven DC approximation problems are demonstrated. The observed convergence behaviors are in accordance with our theoretical results.</p>

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Difference of Convex Approximation for Optimization with Distribution Uncertainty in Chance Constraints

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Hai-Lin Sun,
  • Da-Li Zhang,
  • Yan-Nan Chen

摘要

In this paper, we consider the convergence analysis of data-driven mathematical programs with distributionally robust chance constraints (MPDRCC) without continuity condition of distributionally robust probability functions. Combining with the data-driven approximation, we also propose a difference of convex (DC) approximation method to MPDRCC. We give the convergence analysis of the DC approximation method without continuity condition of distributionally robust probability functions and apply a recent DC algorithm to solve them. In the numerical examples, the convergence trends of data-driven MPDRCC and data-driven DC approximation problems are demonstrated. The observed convergence behaviors are in accordance with our theoretical results.