Approximate Methods for Solving Chance-Constrained Linear Programs in Probability Measure Space
针对概率测度空间中难以求解的机会约束线性规划，首次提出两种可解的近似优化问题，并证明其一致收敛性，通过数值实验验证了方法的有效性。
Abstract 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.