Domain Decomposition for Non-smooth (in Particular TV) Minimization

Andreas Langer

doi:10.1007/978-3-030-03009-4_104-1

Domain Decomposition for Non-smooth (in Particular TV) Minimization

Andreas Langer

Matematik LTH

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Kapitel samlingsverk › Forskning › Peer review

Sammanfattning

Domain decomposition is one of the most efficient techniques to derive efficient methods for large-scale problems. In this chapter such decomposition methods for the minimization of the total variation are discussed. We differ between approaches which directly tackle the (primal) total variation minimization and approaches which deal with their predual formulation. Thereby we mainly concentrate on the presentation of domain decomposition methods which guarantee to converge to a solution of the global problem.

Originalspråk	engelska
Titel på värdpublikation	Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
Redaktörer	Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younces
Förlag	Springer
ISBN (elektroniskt)	978-3-030-03009-4
DOI	https://doi.org/10.1007/978-3-030-03009-4_104-1
Status	Published - 2021

Ämnesklassifikation (UKÄ)

Beräkningsmatematik

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10.1007/978-3-030-03009-4_104-1

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title = "Domain Decomposition for Non-smooth (in Particular TV) Minimization",

abstract = "Domain decomposition is one of the most efficient techniques to derive efficient methods for large-scale problems. In this chapter such decomposition methods for the minimization of the total variation are discussed. We differ between approaches which directly tackle the (primal) total variation minimization and approaches which deal with their predual formulation. Thereby we mainly concentrate on the presentation of domain decomposition methods which guarantee to converge to a solution of the global problem.",

author = "Andreas Langer",

year = "2021",

doi = "10.1007/978-3-030-03009-4_104-1",

language = "English",

editor = "Ke Chen and Carola-Bibiane Sch{\"o}nlieb and Xue-Cheng Tai and Laurent Younces",

booktitle = "Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging",

publisher = "Springer",

address = "Germany",

}

Domain Decomposition for Non-smooth (in Particular TV) Minimization. / Langer, Andreas.
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. red. / Ke Chen; Carola-Bibiane Schönlieb; Xue-Cheng Tai; Laurent Younces. Springer, 2021.

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Kapitel samlingsverk › Forskning › Peer review

TY - CHAP

T1 - Domain Decomposition for Non-smooth (in Particular TV) Minimization

AU - Langer, Andreas

PY - 2021

Y1 - 2021

N2 - Domain decomposition is one of the most efficient techniques to derive efficient methods for large-scale problems. In this chapter such decomposition methods for the minimization of the total variation are discussed. We differ between approaches which directly tackle the (primal) total variation minimization and approaches which deal with their predual formulation. Thereby we mainly concentrate on the presentation of domain decomposition methods which guarantee to converge to a solution of the global problem.

AB - Domain decomposition is one of the most efficient techniques to derive efficient methods for large-scale problems. In this chapter such decomposition methods for the minimization of the total variation are discussed. We differ between approaches which directly tackle the (primal) total variation minimization and approaches which deal with their predual formulation. Thereby we mainly concentrate on the presentation of domain decomposition methods which guarantee to converge to a solution of the global problem.

U2 - 10.1007/978-3-030-03009-4_104-1

DO - 10.1007/978-3-030-03009-4_104-1

M3 - Book chapter

BT - Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

A2 - Chen, Ke

A2 - Schönlieb, Carola-Bibiane

A2 - Tai, Xue-Cheng

A2 - Younces, Laurent

PB - Springer

ER -

Domain Decomposition for Non-smooth (in Particular TV) Minimization

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