一类带概率互补约束的随机优化问题的最优性条件

Optimality conditions for a class of stochastic optimization problems with probabilistic complementarity constraints

主要讨论了一类带概率互补约束的随机优化问题的最优性条件.首先利用一类非线性互补(NCP)函数将概率互补约束转化成为一个通常的概率约束.然后,利用概率约束的相关理论结果,将其等价地转化成一个带不等式约束的优化问题.最后给出了这类问题的弱驻点和最优解的最优性条件.

In this paper, we focus on the optimality conditions for a class of stochastic optimization problem with probabilistic complementarity constraints. By using a kind of nonlinear complementarity （NCP） function, we transform the probabilistic complementary constraint into a chance constraint. By using the theories in chance constraint, we obtain an optimization problem with inequality constraint and then, optimality conditions for weak stationary points and the optimal solutions are given.