Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion

We present a principled way of deriving a continuous relaxation of a given discontinuous shrinkage operator, which is based on two fundamental results, proximal inclusion and conversion. Using our results, the discontinuous operator is converted, via double inversion, to a continuous operator; more precisely, the associated “set-valued” operator is converted to a “single-valued” Lipschitz continuous operator. The first illustrative example is the firm shrinkage operator which can be derived as a continuous relaxation of the hard shrinkage operator. We also derive a new operator as a continuous relaxation of the discontinuous shrinkage operator associated with the so-called reversely ordered weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> (ROWL) penalty. Numerical examples demonstrate potential advantages of the continuous relaxation.

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Publication Details

Journal
IEEE Open Journal of Signal Processing
Published
2025-01-01
DOI
https://doi.org/10.1109/ojsp.2025.3579646
Citations
1
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
1.33
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article

Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion

Masahiro Yukawa
1 citations
IEEE Open Journal of Signal Processing
Advanced Numerical Methods in Computational Mathematics
1.33
article

Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion

Masahiro Yukawa
article en
1 citations

Abstract

We present a principled way of deriving a continuous relaxation of a given discontinuous shrinkage operator, which is based on two fundamental results, proximal inclusion and conversion. Using our results, the discontinuous operator is converted, via double inversion, to a continuous operator; more precisely, the associated “set-valued” operator is converted to a “single-valued” Lipschitz continuous operator. The first illustrative example is the firm shrinkage operator which can be derived as a continuous relaxation of the hard shrinkage operator. We also derive a new operator as a continuous relaxation of the discontinuous shrinkage operator associated with the so-called reversely ordered weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> (ROWL) penalty. Numerical examples demonstrate potential advantages of the continuous relaxation.

IEEE Open Journal of Signal ProcessingVol. 6
Keio University (JP)
Reduced inequalities
Openalex Percentile: Top 22%
Advanced Numerical Methods in Computational Mathematics
1.33
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Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion — Masahiro Yukawa · IEEE Open Journal of Signal Processing (2025) | TGRS Research Map | TGRS