In this paper,we aim to solve an inverse robust optimization problem,in which the parameters in both the objective function and the robust constraint set need to be adjusted as little as possible so that a known feasi...In this paper,we aim to solve an inverse robust optimization problem,in which the parameters in both the objective function and the robust constraint set need to be adjusted as little as possible so that a known feasible solution becomes the optimal one.We formulate this inverse problem as a minimization problem with a linear equality constraint,a second-order cone complementarity constraint and a linear complementarity constraint.A perturbation approach is constructed to solve the inverse problem.An inexact Newton method with Armijo line search is applied to solve the perturbed problem.Finally,the numerical results are reported to show the effectiveness of the approach.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.11571059)the Fundamental Research Funds for the Central Universities(Grant No.DUT16LK30)
文摘In this paper,we aim to solve an inverse robust optimization problem,in which the parameters in both the objective function and the robust constraint set need to be adjusted as little as possible so that a known feasible solution becomes the optimal one.We formulate this inverse problem as a minimization problem with a linear equality constraint,a second-order cone complementarity constraint and a linear complementarity constraint.A perturbation approach is constructed to solve the inverse problem.An inexact Newton method with Armijo line search is applied to solve the perturbed problem.Finally,the numerical results are reported to show the effectiveness of the approach.
