An Effective Algorithm for Quadratic Optimization with Non-Convex Inhomogeneous Quadratic Constraints - Advances in Pure Mathematics

APM > Vol.7 No.4, April 2017

Advances in Pure Mathematics

Volume 7, Issue 4 (April 2017)

ISSN Print: 2160-0368 ISSN Online: 2160-0384

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

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DOI: 10.4236/apm.2017.74018 1,619 Downloads 3,677 Views Citations

Author(s)

Kaiyao Lou

Affiliation(s)

Department of Mathematics, College of Science, Shanghai University, Shanghai, China.

ABSTRACT

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.

KEYWORDS

Nonconvex Inhomogeneous Quadratic Constrained Quadratic Optimization, Semidefinite Programming Relaxation, Np-Hard

Share and Cite:

Lou, K. (2017) An Effective Algorithm for Quadratic Optimization with Non-Convex Inhomogeneous Quadratic Constraints. Advances in Pure Mathematics, 7, 314-323. doi: 10.4236/apm.2017.74018.

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