Domain Decomposition for Non-smooth (in Particular TV) Minimization
Andreas Langer ()
Additional contact information
Andreas Langer: Lund University, Centre for Mathematical Sciences
Chapter 11 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 379-425 from Springer
Abstract:
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.
Keywords: Domain decomposition; Schwarz method; Non-smooth optimisation; Total variation (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_104
Ordering information: This item can be ordered from
http://www.springer.com/9783030986612
DOI: 10.1007/978-3-030-98661-2_104
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().