基于CVaR的逼近算法求解一类随机逆变分不等式

CVaR-based formulation and approximation method for a class of stochastic inverse variational inequality problems

随机逆变分不等式是变分不等式领域的重要分支,其被广泛的应用于交通均衡、网络经济均衡、电力系统、物流供应链管理等实际问题.主要工作是通过定义随机逆变分不等式的正则化间隙函数,研究了基于CVaR的一类随机逆变分不等式的逼近算法,并在一定条件下,运用拟蒙特卡洛方法得到这类随机逆变分不等式的解.

Stochastic inverse variational inequality is an important part of the variational inequalities theory. It is widely applied in traffic equilibrium, network economy equilibrium, electrical power system, logistics management, etc. By defining the regular gap-function,we consider the CVaR-based formulation and approximation method for a class of stochastic inverse variational inequality problems. By employing the quasi-Monte Carlo method, we find a solution of a class of stochastic inverse variational inequality problems under some suitable conditions.