A deterministic global optimization algorithm based on a linearizing method for nonconvex quadratically constrained programs

In this paper a deterministic global optimization algorithm for solving nonconvex quadratically constrained quadratic programs (NQP) is proposed. Utilizing a new linearizing method, the initial nonlinear and nonconvex NQP problem is reduced to a sequence of linear programming problems. The proposed algorithm is proven to be convergent to the global minimum through the solutions of a series of linear programming problems. Several NQP examples in the literatures are tested to demonstrate that the proposed method can systematically solve these examples to find the global optimum within a prespecified error.