A global optimization algorithm for generalized quadratic programming

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. © 2013 hongwei jiao and yongqiang chen.