A sequential constraint relaxation algorithm for rank-one constrained problems

Pan Cao, John Thompson, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many optimization problems in communications and signal processing can be formulated as rank-one constrained optimization problems. This has motivated the development of methods to solve such problem in specific scenarios. However, due to the non-convex nature of the rank-one constraint, limited progress has been made in solving generic rank-one constrained optimization problems. In particular, the problem of efficiently finding a locally optimal solution to a generic rankone constrained problem remains open. This paper focuses on solving general rank-one constrained problems via relaxation techniques. However, instead of dropping the rank-one constraint completely as is done in traditional rank-one relaxation methods, a novel algorithm that gradually relaxes the rank-one constraint, termed the sequential rank-one constraint relaxation (SROCR) algorithm, is proposed. Compared with previous algorithms, the SROCR algorithm can solve general rank-one constrained problems, and can find feasible solutions with favorable complexity.

Original languageEnglish (US)
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1060-1064
Number of pages5
ISBN (Electronic)9780992862671
DOIs
StatePublished - Oct 23 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: Aug 28 2017Sep 2 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Other

Other25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period8/28/179/2/17

All Science Journal Classification (ASJC) codes

  • Signal Processing

Fingerprint Dive into the research topics of 'A sequential constraint relaxation algorithm for rank-one constrained problems'. Together they form a unique fingerprint.

  • Cite this

    Cao, P., Thompson, J., & Poor, H. V. (2017). A sequential constraint relaxation algorithm for rank-one constrained problems. In 25th European Signal Processing Conference, EUSIPCO 2017 (pp. 1060-1064). [8081370] (25th European Signal Processing Conference, EUSIPCO 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/EUSIPCO.2017.8081370