An Approximation Algorithm For Robust Chance Constrained Optimization Problems

Although stochastic programming usually given distribution function, in practice it is d-ifficult to determine the distribution function, while the robust optimization problem discusses the uncertainty of the distribution function as a set. This paper discussed a type of robust chance constrained optimization problem with uncertainty distribution, in the circumstances of knowing first moment and second moment information in this article, use the duality theorem problem into a non-convex form of semi-definite programming problem, and the use of homoge-neous tips to turn it into a linear semi-definite programming problem, the final calculated using the relevant software to get the approximate optimal solution by comparing with the classical mean-variance model, we derive the opportunity to use actual data constrained optimization model with robust risk control and revenue maximizing dual role.