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Journal of Fourier Analysis and Applications

, Volume 14, Issue 5–6, pp 813–837 | Cite as

Linear Convergence of Iterative Soft-Thresholding

  • Kristian Bredies
  • Dirk A. LorenzEmail author
Article

Abstract

In this article a unified approach to iterative soft-thresholding algorithms for the solution of linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate the algorithm in the framework of generalized gradient methods and present a new convergence analysis. As main result we show that the algorithm converges with linear rate as soon as the underlying operator satisfies the so-called finite basis injectivity property or the minimizer possesses a so-called strict sparsity pattern. Moreover it is shown that the constants can be calculated explicitly in special cases (i.e. for compact operators). Furthermore, the techniques also can be used to establish linear convergence for related methods such as the iterative thresholding algorithm for joint sparsity and the accelerated gradient projection method.

Keywords

Iterative soft-thresholding Inverse problems Sparsity constraints Convergence analysis Generalized gradient projection method 

Mathematics Subject Classification (2000)

65J22 46N10 49M05 

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Copyright information

© Birkhäuser Boston 2008

Authors and Affiliations

  1. 1.Center for Industrial Mathematics / Fachbereich 3University of BremenBremenGermany

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