A new bound-and-reduce approach of nonconvex quadratic programming problems
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摘要
For the nonconvex quadratic programming problem, a new linear programming relaxation bound-and-reduce algorithm is proposed and its convergence is proved. In this algorithm, a new hyper-rectangle partition technique and a new linear programming relaxation tactics are used. At the same time, the hyper-rectangular reduction method is used to raise its convergent speed. The numerical results demonstrate the effectiveness and feasibility of the proposed algorithm.
论文关键词:Nonconvex quadratic programming,Global optimization,Branch-and-bound,Relaxation technique,Hyper-rectangle reducing
论文评审过程:Available online 18 November 2014.
论文官网地址:https://doi.org/10.1016/j.amc.2014.10.077