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 language | English (US) |
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Title of host publication | Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 401-405 |
Number of pages | 5 |
ISBN (Electronic) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2016-February |
ISSN (Print) | 1058-6393 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.