An Effective Algorithm for Quadratic Optimization with Non-Convex Inhomogeneous Quadratic Constraints  

作　　者：Kaiyao Lou 

机构地区：[1]Department of Mathematics, College of Science, Shanghai University, Shanghai, China

出　　处：《Advances in Pure Mathematics》2017年第4期314-323,共10页理论数学进展（英文）

摘　　要：This paper considers the NP (Non-deterministic Polynomial)-hard problem of finding a minimum value of a quadratic program (QP), subject to m non-convex inhomogeneous quadratic constraints. One effective algorithm is proposed to get a feasible solution based on the optimal solution of its semidefinite programming (SDP) relaxation problem.This paper considers the NP (Non-deterministic Polynomial)-hard problem of finding a minimum value of a quadratic program (QP), subject to m non-convex inhomogeneous quadratic constraints. One effective algorithm is proposed to get a feasible solution based on the optimal solution of its semidefinite programming (SDP) relaxation problem.

关 键 词：NONCONVEX INHOMOGENEOUS QUADRATIC Constrained QUADRATIC Optimization SEMIDEFINITE Programming RELAXATION NP-HARD 

分 类 号：O1[理学—数学]