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A parallel multigrid method for constrained minimization problems and its application to friction, contact, and obstacle problems

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Computing and Visualization in Science

Abstract

The parallel solution of constrained minimization problems requires special care to be taken with respect to the information transfer between the different subproblems. Here, we present a nonlinear decomposition approach which employs an additional nonlinear correction step along the processor interfaces. Our approach is generic in the sense that it can be applied to a wide class of minimization problems with strongly local nonlinearities, including even nonsmooth minimization problems. We also describe the implementation of our nonlinear decomposition method in the object oriented library ObsLib \(++\). The flexibility of our approach and its implementation is presented along different problem classes as obstacle problems, frictional contact problems and biomechanical applications. For the same examples, number of iterations, computation time, and parallelization speedup are measured, and the results demonstrate that the implementation scales reasonably well up to 4096 processors.

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Acknowledgments

The authors thank C. Groß and T. Dickopf.

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Authors and Affiliations

  1. Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland

    Rolf Krause, Alessandro Rigazzi & Johannes Steiner

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  1. Rolf Krause
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  2. Alessandro Rigazzi
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  3. Johannes Steiner
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Correspondence to Rolf Krause.

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Communicated by Gabriel Wittum.

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Krause, R., Rigazzi, A. & Steiner, J. A parallel multigrid method for constrained minimization problems and its application to friction, contact, and obstacle problems. Comput. Visual Sci. 18, 1–15 (2016). https://doi.org/10.1007/s00791-016-0267-1

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  • DOI: https://doi.org/10.1007/s00791-016-0267-1

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