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DEGENERATE PRECONDITIONED PROXIMAL POINT ALGORITHMS.

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Title: DEGENERATE PRECONDITIONED PROXIMAL POINT ALGORITHMS.
Authors: BREDIES, KRISTIAN1 kristian.bredies@uni-graz.at, CHENCHENE, ENIS1 enis.chenchene@uni-graz.at, LORENZ, DIRK A.2 d.lorenz@tu-braunschweig.de, NALDI, EMANUELE2 e.naldi@tu-braunschweig.de
Source: SIAM Journal on Optimization. 2022, Vol. 32 Issue 3, p2376-2401. 26p.
Subjects: Algorithms, Nonsmooth optimization, Monotone operators, Generalization
Abstract: In this paper we describe a systematic procedure to analyze the convergence of degenerate preconditioned proximal point algorithms. We establish weak convergence results under mild assumptions that can be easily employed in the context of splitting methods for monotone inclusion and convex minimization problems. Moreover, we show that the degeneracy of the preconditioner allows for a reduction of the variables involved in the iteration updates. We show the strength of the proposed framework in the context of splitting algorithms, providing new simplified proofs of convergence and highlighting the link between existing schemes, such as Chambolle-Pock, forward Douglas-Rachford, and Peaceman-Rachford, that we study from a preconditioned proximal point perspective. The proposed framework allows us to devise new flexible schemes and provides new ways to generalize existing splitting schemes to the case of the sum of many terms. As an example, we present a new sequential generalization of forward Douglas-Rachford along with numerical experiments that demonstrates its interest in the context of nonsmooth convex optimization. [ABSTRACT FROM AUTHOR]
Copyright of SIAM Journal on Optimization is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: DEGENERATE PRECONDITIONED PROXIMAL POINT ALGORITHMS.
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  Data: In this paper we describe a systematic procedure to analyze the convergence of degenerate preconditioned proximal point algorithms. We establish weak convergence results under mild assumptions that can be easily employed in the context of splitting methods for monotone inclusion and convex minimization problems. Moreover, we show that the degeneracy of the preconditioner allows for a reduction of the variables involved in the iteration updates. We show the strength of the proposed framework in the context of splitting algorithms, providing new simplified proofs of convergence and highlighting the link between existing schemes, such as Chambolle-Pock, forward Douglas-Rachford, and Peaceman-Rachford, that we study from a preconditioned proximal point perspective. The proposed framework allows us to devise new flexible schemes and provides new ways to generalize existing splitting schemes to the case of the sum of many terms. As an example, we present a new sequential generalization of forward Douglas-Rachford along with numerical experiments that demonstrates its interest in the context of nonsmooth convex optimization. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SIAM Journal on Optimization is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1137/21M1448112
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      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 2376
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Nonsmooth optimization
        Type: general
      – SubjectFull: Monotone operators
        Type: general
      – SubjectFull: Generalization
        Type: general
    Titles:
      – TitleFull: DEGENERATE PRECONDITIONED PROXIMAL POINT ALGORITHMS.
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            NameFull: LORENZ, DIRK A.
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            – D: 01
              M: 07
              Text: 2022
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              Y: 2022
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