Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20108
Title: Semidefinite relaxation bounds for bi-quadratic optimization problems with quadratic constraints
Authors: Zhang, X
Ling, C
Qi, L 
Keywords: Approximation solution
Bi-quadratic optimization
Probabilistic solution
Semidefinite programming relaxation
Issue Date: 2011
Publisher: Springer
Source: Journal of global optimization, 2011, v. 49, no. 2, p. 293-311 How to cite?
Journal: Journal of global optimization 
Abstract: This paper studies the relationship between the so-called bi-quadratic optimization problem and its semidefinite programming (SDP) relaxation. It is shown that each r-bound approximation solution of the relaxed bi-linear SDP can be used to generate in randomized polynomial time an O(r) -approximation solution of the original bi-quadratic optimization problem, where the constant in O(r) does not involve the dimension of variables and the data of problems. For special cases of maximization model, we provide an approximation algorithm for the considered problems.
URI: http://hdl.handle.net/10397/20108
ISSN: 0925-5001
EISSN: 1573-2916
DOI: 10.1007/s10898-010-9545-5
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