Robust Optimization Problem And Application Under The Condition Of Moment Uncertainty

In such financial domains as project control, supply chain management, game theory and portfolio, the parameter uncertainty of constraint optimization problem has the extremely widespread application, and at the same time it is also one of the hot research fields in mathematical programming. Robust optimization method is a main method for dealing with uncertain parameters, Robust Optimization, a kind of optimization solution, can be fully protected against data uncertainty optimization method. The essence of robust optimization can make process parameter uncertainty into the direct and relatively simple form, the original problem into the deterministic optimization problem easy to be solved,which makes the solution to the parameter changing within a certain range of maximum close to the optimal solution. Portfolio of stochastic optimization model, for example, with minor changes in parameters may have great effect on optimization results, and the optimal solution to the robust optimization model can still maintain the optimality of the results with parameters changing in a certain range.This paper mainly studies the robust optimization method in the application of the portfolio. Main work involves the following:1. Studied when the moment and distribution of information of the random variables are uncertain, with LPM method to measure the risk value of robust optimization method, we prove that the corresponding robust optimization model can be converted into a second-order cone optimization problem.2. Studied in carbon emissions constraints, based on the optimization problem of the power suppliers with the wost condition Va R Model, and which is equivalent to the corresponding second-order cone optimization problem, and numerical experiments are given as well.3. studied when the information of the random variables are known, the condition of two different portfolios under the uncertain set of robust optimization problems, we have analyzed the model.4.On condition that the random variables information and distribution information are uncertain, we have set up a robust optimization model of portfolio options, and proved its feasibility.