Semidefinite relaxation of quadratic optimization problems

Zhi Quan Luo; Wing Kin Ma; Anthony So; Yinyu Ye; Shuzhong Zhang

doi:10.1109/MSP.2010.936019

Semidefinite relaxation of quadratic optimization problems

Zhi Quan Luo, Wing Kin Ma, Anthony So, Yinyu Ye, Shuzhong Zhang

Research output: Contribution to journal › Article › peer-review

2909 Scopus citations

Abstract

In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very exciting developments in the area of signal processing and communications, and it has shown great significance and relevance on a variety of applications. Roughly speaking, SDR is a powerful, computationally efficient approximation technique for a host of very difficult optimization problems. In particular, it can be applied to many nonconvex quadratically constrained quadratic programs (QCQPs) in an almost mechanical fashion, including the following problem:

Original language	English (US)
Article number	5447068
Pages (from-to)	20-34
Number of pages	15
Journal	IEEE Signal Processing Magazine
Volume	27
Issue number	3
DOIs	https://doi.org/10.1109/MSP.2010.936019
State	Published - May 2010

Bibliographical note

Funding Information:
This work is supported in part by Hong Kong Research Grants Council (RGC) General Research Funds (GRFs), project numbers CUHK415908, CUHK416908, and CUHK419208; by the Army Research Office, grant number W911NF-09-1-0279; and by the National Science Foundation, grant number CMMI-0726336.

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Semidefinite relaxation of quadratic optimization problems

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