Further Results on Approximating Nonconvex Quadratic Optimization by Semidefinite Programming Relaxation

We study approximation bounds for the semidefinite programming (SDP) relaxation ofquadratically constrained quadratic optimization: $\min f^0(x)$ subject to $f^k(x)\le 0$, $k=1,\dots,m$, where fk(x)=xTAkx+(bk)Tx+ck. In the special case of ellipsoid constraints with interior feasible solution at 0, w...

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Bibliographic Details

Published in:	SIAM journal on optimization Vol. 14; no. 1; pp. 268 - 283
Main Author:	Tseng, Paul
Format:	Journal Article
Language:	English
Published:	Philadelphia Society for Industrial and Applied Mathematics 01.01.2003
Subjects:	Approximation Optimization Semidefinite programming
ISSN:	1052-6234, 1095-7189
Online Access:	Get full text
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