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

An Effective Algorithm for Quadratic Optimization with Non-Convex Inhomogeneous Quadratic Constraints
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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.
作者 Kaiyao Lou
出处 《Advances in Pure Mathematics》 2017年第4期314-323,共10页 理论数学进展(英文)
关键词 NONCONVEX INHOMOGENEOUS QUADRATIC Constrained QUADRATIC Optimization SEMIDEFINITE Programming RELAXATION NP-HARD Nonconvex Inhomogeneous Quadratic Constrained Quadratic Optimization Semidefinite Programming Relaxation Np-Hard
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