论文标题
在概率测量空间中解决机会限制线性程序的近似方法
Approximate Methods for Solving Chance Constrained Linear Programs in Probability Measure Space
论文作者
论文摘要
在概率度量空间中，可以将风险感知的决策问题作为偶然受限的线性程序提出。概率测量空间中的机会约束线性程序是棘手的，并且没有数值方法来解决此问题。本文介绍了第一次以概率测量空间来解决机会约束的线性程序的数值方法。我们提出了两个可解决的优化问题，作为原始问题的近似问题。我们证明了每个近似问题的均匀收敛性。此外，已经实施了数值实验来验证所提出的方法。
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.
