A Compact Neural Network for Fused Lasso Signal Approximator

Majid Mohammadi*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
26 Downloads (Pure)


The fused lasso signal approximator (FLSA) is a vital optimization problem with extensive applications in signal processing and biomedical engineering. However, the optimization problem is difficult to solve since it is both nonsmooth and nonseparable. The existing numerical solutions implicate the use of several auxiliary variables in order to deal with the nondifferentiable penalty. Thus, the resulting algorithms are both time- and memory-inefficient. This paper proposes a compact neural network to solve the FLSA. The neural network has a one-layer structure with the number of neurons proportionate to the dimension of the given signal, thanks to the utilization of consecutive projections. The proposed neural network is stable in the Lyapunov sense and is guaranteed to converge globally to the optimal solution of the FLSA. Experiments on several applications from signal processing and biomedical engineering confirm the reasonable performance of the proposed neural network.

Original languageEnglish
Article number8766144
Pages (from-to)4327-4336
Number of pages10
JournalIEEE Transactions on Cybernetics
Issue number8
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript


  • Fused lasso
  • global convergence
  • Lyapunov
  • neural network


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