A neurodynamic optimization approach to constrained sparsity maximization based on alternative objective functions

Zhishan Guo, Jun Wang

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

16 Citations (Scopus)

Abstract

In recent years, constrained sparsity maximization problems received tremendous attention in the context of compressive sensing. Because the formulated constrained L0 norm minimization problem is NP-hard, constrained L1 norm minimization is usually used to compute approximate sparse solutions. In this paper, we introduce several alternative objective functions, such as weighted L1 norm, Laplacian, hyperbolic secant, and Gaussian functions, as approximations of the L0 norm. A one-layer recurrent neural network is applied to compute the optimal solutions to the reformulated constrained minimization problems subject to equality constraints. Simulation results in terms of time responses, phase diagrams, and tabular data are provided to demonstrate the superior performance of the proposed neurodynamic optimization approach to constrained sparsity maximization based on the problem reformulations.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/TerritorySpain
CityBarcelona
Period18/07/1023/07/10

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