此外‚用稳健优化求解模型1的思路是通过转换原问题的数学结构‚把不确定性

In addition, the idea of ​​using robust optimization to solve Model 1 is to transform the mathematical structure of the original problem into a deterministic problem, but the mathematical characteristics of the original problem cannot be maintained in the process of structure transformation. For example, model 1 is a linear programming problem, while model 2 is a conic quadratic programming problem. Obviously, "the optimal solution of the transformed problem may not be the optimal solution of the original problem, even very different. A numerical counterexample can be used to verify this conclusion.

in addition ‚ The idea of solving model 1 with robust optimization is to transform the mathematical structure of the original problem ‚ Turning uncertainty into certainty ‚ However, the mathematical characteristics of the original problem can not be maintained in the process of structural transformation. For example, model 1 is a linear programming problem ‚ Model 2 is a conic quadratic programming problem ‚ The optimal solution of the transformed problem may not be the optimal solution of the original problem ‚ Even very different ‚ This conclusion can be verified by a numerical counterexample

In addition, the idea of using robust optimization to solve model 1 is to transform the uncertain problem into a deterministic problem by transforming the mathematical structure of the original problem, but the mathematical characteristics of the original problem can not be maintained in the process of structural transformation. For example, model 1 is a linear programming problem and model 2 is a cone quadratic programming problem. Obviously, the optimal solution of the transformed problem is probably not the optimal solution of the original problem or even quite different. A numerical counterexample can be used to verify this conclusion.