Yang, Yang ; Pesavento, Marius ; Chatzinotas, Symeon ; Ottersten, Björn (2018)
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations.
10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Sheffield, United Kingdom (08.07.2018-11.07.2018)
doi: 10.1109/SAM.2018.8448866
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, we propose a successive convex approximation framework for sparse optimization where the nondifferentiable regular- ization in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization method and successive convex approximation for nonconvex optimization where the regularization function is convex. The proposed framework is flexible and it leads to algorithms that exploit the problem structure and have a low complexity. We demonstrate these advantages by an example application where the nonconvex regularization is the capped l1-norm function. Customizing the proposed framework, we obtain a best-response type algorithm for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. Finally, the proposed algorithms are numerically tested.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Yang, Yang ; Pesavento, Marius ; Chatzinotas, Symeon ; Ottersten, Björn |
| Art des Eintrags: | Bibliographie |
| Titel: | Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations |
| Sprache: | Englisch |
| Publikationsjahr: | 30 August 2018 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings of the Tenth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) |
| Veranstaltungstitel: | 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) |
| Veranstaltungsort: | Sheffield, United Kingdom |
| Veranstaltungsdatum: | 08.07.2018-11.07.2018 |
| DOI: | 10.1109/SAM.2018.8448866 |
| Kurzbeschreibung (Abstract): | In this paper, we propose a successive convex approximation framework for sparse optimization where the nondifferentiable regular- ization in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization method and successive convex approximation for nonconvex optimization where the regularization function is convex. The proposed framework is flexible and it leads to algorithms that exploit the problem structure and have a low complexity. We demonstrate these advantages by an example application where the nonconvex regularization is the capped l1-norm function. Customizing the proposed framework, we obtain a best-response type algorithm for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. Finally, the proposed algorithms are numerically tested. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 25 Okt 2018 10:22 |
| Letzte Änderung: | 27 Sep 2024 09:58 |
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