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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1841-1845
Number of pages5
ISBN (Electronic)9789082797091
Publication statusPublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 2022 Aug 292022 Sep 2

Publication series

NameEuropean Signal Processing Conference
Volume2022-August
ISSN (Print)2219-5491

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
Country/TerritorySerbia
CityBelgrade
Period22/8/2922/9/2

Keywords

  • distributed optimization
  • Moreau envelope
  • nonconvex penalty
  • proximity operator
  • sparseness

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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