Distributed continuous-time accelerated neurodynamic approaches for sparse recovery via smooth approximation to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e192"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-minimization-Reference-Cited by-同舟云学术

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Distributed continuous-time accelerated neurodynamic approaches for sparse recovery via smooth approximation to L1-minimization

Published:2024-04 Issue: Volume:172 Page:106123
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Xu Junpeng,He Xing

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Reference40 articles.

1. Fast and accurate algorithms for re-weighted ℓ1-norm minimization;Asif;IEEE Transactions on Signal Processing,2013

2. Nonlinear programming: theory and algorithms;Bazaraa,2013

3. NESTA: A fast and accurate first-order method for sparse recovery;Becker;SIAM Journal on Imaging Sciences,2011

4. Decoding by linear programming;Candes;IEEE Transactions on Information Theory,2005

5. Bicriteria sparse nonnegative matrix factorization via two-timescale duplex neurodynamic optimization;Che;IEEE Transactions on Neural Networks and Learning Systems,2023

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