Literature DB >> 29805242

ANOTHER LOOK AT THE FAST ITERATIVE SHRINKAGE/THRESHOLDING ALGORITHM (FISTA).

Donghwan Kim1, Jeffrey A Fessler1.   

Abstract

This paper provides a new way of developing the "Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)" [3] that is widely used for minimizing composite convex functions with a nonsmooth term such as the ℓ1 regularizer. In particular, this paper shows that FISTA corresponds to an optimized approach to accelerating the proximal gradient method with respect to a worst-case bound of the cost function. This paper then proposes a new algorithm that is derived by instead optimizing the step coefficients of the proximal gradient method with respect to a worst-case bound of the composite gradient mapping. The proof is based on the worst-case analysis called Performance Estimation Problem in [11].

Entities:  

Year:  2018        PMID: 29805242      PMCID: PMC5966151          DOI: 10.1137/16M108940X

Source DB:  PubMed          Journal:  SIAM J Optim        ISSN: 1052-6234            Impact factor:   2.850


  3 in total

1.  Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems.

Authors:  Amir Beck; Marc Teboulle
Journal:  IEEE Trans Image Process       Date:  2009-07-24       Impact factor: 10.856

2.  On the Convergence Analysis of the Optimized Gradient Method.

Authors:  Donghwan Kim; Jeffrey A Fessler
Journal:  J Optim Theory Appl       Date:  2016-10-05       Impact factor: 2.249

3.  Optimized first-order methods for smooth convex minimization.

Authors:  Donghwan Kim; Jeffrey A Fessler
Journal:  Math Program       Date:  2015-10-17       Impact factor: 3.995

  3 in total
  1 in total

1.  Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging.

Authors:  Daniel S Weller; Douglas C Noll; Jeffrey A Fessler
Journal:  Signal Processing       Date:  2018-12-03       Impact factor: 4.662

  1 in total

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