A Global Optimization Algorithm for Generalized Quadratic Programming

Abstract EN

We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints.

By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxation linear programming problems.

To improve the computational efficiency of the algorithm, a range reduction technique is employed in the branch and bound procedure.

The proposed algorithm is convergent to the global minimum of the (GQP) by means of the subsequent solutions of a series of relaxation linear programming problems.

Finally, numerical results show the robustness and effectiveness of the proposed algorithm.