Smoothing Strategy Along with Conjugate Gradient Algorithm for Signal Reconstruction

Caiying Wu, Jing Wang, Jan Harold Alcantara, Jein Shan Chen

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a new smoothing strategy along with conjugate gradient algorithm for the signal reconstruction problem. Theoretically, the proposed conjugate gradient algorithm along with the smoothing functions for the absolute value function is shown to possess some nice properties which guarantee global convergence. Numerical experiments and comparisons suggest that the proposed algorithm is an efficient approach for sparse recovery. Moreover, we demonstrate that the approach has some advantages over some existing solvers for the signal reconstruction problem.

Original languageEnglish
Article number21
JournalJournal of Scientific Computing
Volume87
Issue number1
DOIs
Publication statusPublished - 2021 Apr

Keywords

  • Conjugate gradient algorithm
  • l-norm regularization
  • Signal recovery
  • Sparse solution

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Numerical Analysis
  • Engineering(all)
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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