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

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 ℓ regularizer. In particular, this paper shows that FISTA corresponds to an optimize...

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Veröffentlicht in:SIAM journal on optimization Jg. 28; H. 1; S. 223
Hauptverfasser: Kim, Donghwan, Fessler, Jeffrey A
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 2018
ISSN:1052-6234
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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 ℓ 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].
AbstractList 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].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].
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 ℓ 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].
Author Kim, Donghwan
Fessler, Jeffrey A
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  givenname: Jeffrey A
  surname: Fessler
  fullname: Fessler, Jeffrey A
  organization: Dept. of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29805242$$D View this record in MEDLINE/PubMed
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Title ANOTHER LOOK AT THE FAST ITERATIVE SHRINKAGE/THRESHOLDING ALGORITHM (FISTA)
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