Abstract
Many problems of recent interest in signal processing, machine learning and wireless communications can be posed as nonconvex nonsmooth optimization problems. These problems are generally difficult to solve especially when the optimization variables are nonlinearly coupled in some nonconvex constraints. In this paper, we propose an algorithm named "penalty dual decomposition" (PDD) method, for the minimization of a nonconvex nonsmooth objective subject to nonconvex constraints. We show that the PDD converges to KKT solutions under certain constraint qualification condition. Simulations corroborate the excellent performance of the PDD method.
| Original language | English (US) |
|---|---|
| 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 | 4059-4063 |
| 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 |
|---|---|
| ISSN (Print) | 1520-6149 |
Other
| Other | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 3/5/17 → 3/9/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.