Abstract
In this paper a global optimization algorithm based on a parametric linearizing method for generalized quadratic programming (GQP), i.e., the quadratic programming problem with nonconvex quadratic constraints, is proposed. By utilizing the linearizing method initial nonconvex nonlinear problem GQP is reduced to a sequence of linear programming problems through the successive refinement of a linear relaxation of feasible region and of the objective function. The proposed algorithm is convergent to the global minimum of GQP by means of the subsequent solutions of a series of linear programming problems. Test results indicate that the proposed algorithm is extremely robust and can be used successfully to solve global minimum of GQP on a microcomputer.
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