A neurodynamic approach to L0-constrained optimization

Yadi Wang, Xiaoping Li, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

L0-constrained optimization problem has been widely applied to sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) in signal processing, statistics, and related fields. However, it is an NP-hard problem to obtain the optimal solution of the L0-constrained optimization problem in the recovery of compressive sensed signals. In this paper, we apply neurodynamic approach to solve the L0-constrained optimization problem. Based on an inverted Gaussian function, a function of L0 approximation can be obtained and a three-layer projection-type neurodynamic model is further constructed. The L0 approximation constraint condition and a convexity objective function under which the neurodynamic optimization method is guaranteed to achieve the locally convergent optimal solution. Experimental results illustrate the effectiveness of the proposed neurodynamic approach.

Original languageEnglish
Title of host publication12th International Conference on Advanced Computational Intelligence, ICACI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages44-50
Number of pages7
ISBN (Electronic)9781728142487
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes
Event12th International Conference on Advanced Computational Intelligence, ICACI 2020 - Dali, Yunnan, China
Duration: 14 Aug 202016 Aug 2020

Publication series

Name12th International Conference on Advanced Computational Intelligence, ICACI 2020

Conference

Conference12th International Conference on Advanced Computational Intelligence, ICACI 2020
Country/TerritoryChina
CityDali, Yunnan
Period14/08/2016/08/20

Keywords

  • Inverted Gaussian function
  • L0-constrained optimization
  • Neurodynamic approach
  • Sparse signal reconstruction

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