Max-min feasible point pursuit for non-convex QCQP

Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos

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

1 Scopus citations

Abstract

Quadratically constrained quadratic programming (QCQP) has a variety of applications in signal processing, communications, and networking - but in many cases the associated QCQP is non-convex and NP-hard. In such cases, semidefinite relaxation (SDR) followed by randomization, or successive convex approximation (SCA) are typically used for approximation. SDR and SCA work with one-sided non-convex constraints, but typically fail to produce a feasible point when there are two-sided or more generally indefinite constraints. A feasible point pursuit (FPP-SCA) algorithm that combines SCA with judicious use of slack variables and a penalty term was recently proposed to obtain feasible and near-optimal solutions with high probability in these difficult cases. In this contribution, we revisit FPP- SCA from a different point of view and recast the feasibility problem in a simpler, more compact way. Simulations show that the new approach outperforms the original FPP-SCA under certain conditions, thus providing a useful addition to our non-convex QCQP toolbox.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages401-405
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

Fingerprint Dive into the research topics of 'Max-min feasible point pursuit for non-convex QCQP'. Together they form a unique fingerprint.

Cite this