Abstract
Quadratically Constrained Quadratic Programming (QCQP) is NP-hard in its general non-convex form, but it frequently arises in engineering design and applications ranging from state estimation to beamforming and clustering. Several polynomial-time approximation algorithms exist for non-convex QCQP problems (QCQPs), but their success hinges upon the ability to find at least one feasible point - which is also hard for a general problem instance. In this paper, we present a framework for computing feasible points of general non-convex QCQPs using simple first-order methods. Our approach features low computational and memory requirements, which makes it well-suited for application on large-scale problems. Experiments indicate the empirical effectiveness of our approach, despite currently lacking theoretical guarantees.
Original language | English (US) |
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Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4064-4068 |
Number of pages | 5 |
ISBN (Electronic) | 9781509041176 |
DOIs | |
State | Published - Jun 16 2017 |
Event | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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ISSN (Print) | 1520-6149 |
Other
Other | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 |
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Country/Territory | United States |
City | New Orleans |
Period | 3/5/17 → 3/9/17 |
Bibliographical note
Funding Information:Supported in part by NSF CIF-1525194, and the Univ. of Minnesota through a Doctoral Dissertation Fellowship.
Publisher Copyright:
© 2017 IEEE.