Distributed Sparse Optimization Based on Minimax Concave and Consensus Promoting Penalties: Towards Global Optimality

Kei Komuro, Masahiro Yukawa, Renato L.G. Cavalcante

研究成果: Conference contribution

抄録

We propose a distributed optimization framework to generate accurate sparse estimates while allowing an algorithmic solution with guaranteed convergence to a global minimizer. To this end, the proposed problem formulation involves the minimax concave penalty together with an additional penalty called consensus promoting penalty (CPP) that induces convexity to the resulting optimization problem. This problem is solved with an exact first-order proximal gradient algorithm, which employs a pair of proximity operators and is referred to as the distributed proximal and debiasing-gradient (DPD) method. Numerical examples show that CPP not only convexifies the whole cost function, but it also accelerates the convergence speed with respect to the system mismatch.

本文言語English
ホスト出版物のタイトル30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ1841-1845
ページ数5
ISBN(電子版)9789082797091
出版ステータスPublished - 2022
イベント30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
継続期間: 2022 8月 292022 9月 2

出版物シリーズ

名前European Signal Processing Conference
2022-August
ISSN(印刷版)2219-5491

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
国/地域Serbia
CityBelgrade
Period22/8/2922/9/2

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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