# Approximate Methods for Solving Chance Constrained Linear Programs in Probability Measure Space

arXiv: https://arxiv.org/abs/2207.09651 · HTML: https://mlsanity.com/paper/2207.09651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Flipping-based Policy for Chance-Constrained Markov Decision Processes](https://mlsanity.com/paper/2410.06474/) (2024)

## Abstract (arXiv)

A risk-aware decision-making problem can be formulated as a chance-constrained linear program in probability measure space. Chance-constrained linear program in probability measure space is intractable, and no numerical method exists to solve this problem. This paper presents numerical methods to solve chance-constrained linear programs in probability measure space for the first time. We propose two solvable optimization problems as approximate problems of the original problem. We prove the uniform convergence of each approximate problem. Moreover, numerical experiments have been implemented to validate the proposed methods.