Towards Implementations of Successive Convex Relaxation Methods for Nonconvex Quadratic Optimization Problems

Recently Kojima and Tun cel proposed new successive convex relaxation methods and their localized-discretized variants for general nonconvex quadratic optimization problems. Although an upper bound of the optimal objective function value within a previously given precision can be found theoretically by solving a nite number of linear programs, several important implementation issues remain unsolved. In this paper, we discuss those issues, present practically implementable algorithms and report numerical results.