Semi-definite programming relaxation for optimal trade-off portfolio selection with sensitivity of parameters
In this paper,we consider the optimal trade-off portfolio problem with parameter sensitivity.For this problem,the model is a non-convex and non-differentiable optimization problem in which the objective function contains the maximum and minimum functions.This optimization problem is transformed into an equivalent non-convex quadratically constrained quadratic programming problem.A tight semi-definite programming relaxation for the equivalent transformation problem is proposed and the gap between it and the original problem is estimated.The numerical results show that the semi-definite programming relaxation can effectively find the global optimal solution of most test problems,and the computational time is less than that of the solver GUROBI.It can provide a method for finding a good approximate solution to the problem.
sensitivity of parametersportfolio selectionnon-convex quadratically constrained quadratic programmingsemi-definite programming relaxationGUROBI
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