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International Conference on Domain Decomposition Methods

DD 2018: Domain Decomposition Methods in Science and Engineering XXV pp 268–276Cite as

Coarse Spaces for Nonlinear Schwarz Methods on Unstructured Grids

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE,volume 138)

Abstract

To solve (1), we consider nonlinear domain decomposition methods of the Schwarz type, e.g., ASPIN (Additive Schwarz Preconditioned Inexact Newton) [1, 10] or RASPEN (Restricted Additive Schwarz Preconditioned Exact Newton) [3].

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cologne, Weyertal 86-90, 50931, Köln, Germany

    Alexander Heinlein & Martin Lanser

  2. Center for Data and Simulation Science, University of Cologne, Köln, Germany

    Alexander Heinlein & Martin Lanser

Authors
  1. Alexander Heinlein
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  2. Martin Lanser
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Corresponding author

Correspondence to Alexander Heinlein .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL, Canada

    Prof. Dr. Ronald Haynes

  2. Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL, Canada

    Prof. Dr. Scott MacLachlan

  3. Department of Computer Science, University of Colorado, Boulder, CO, USA

    Prof. Xiao-Chuan Cai

  4. Laboratoire Analyse, Geometrie, Université Paris XIII, Villetaneuse, France

    Prof. Laurence Halpern

  5. Department of Applied Mathematics, Kyung Hee University, Yongin, Korea (Republic of)

    Prof. Hyea Hyun Kim

  6. Mathematisches Institut, Universität zu Köln, Köln, Germany

    Prof. Axel Klawonn

  7. New York University, New York, NY, USA

    Prof. Dr. Olof Widlund

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Heinlein, A., Lanser, M. (2020). Coarse Spaces for Nonlinear Schwarz Methods on Unstructured Grids. In: , et al. Domain Decomposition Methods in Science and Engineering XXV. DD 2018. Lecture Notes in Computational Science and Engineering, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-030-56750-7_30

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