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Tensor-Krylov methods for large nonlinear equations

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Abstract

In this paper, we describe tensor methods for large systems of nonlinear equations based on Krylov subspace techniques for approximately solving the linear systems that are required in each tensor iteration. We refer to a method in this class as a tensor-Krylov algorithm. We describe comparative testing for a tensor-Krylov implementation versus an analogous implementation based on a Newton-Krylov method. The test results show that tensor-Krylov methods are much more efficient and robust than Newton-Krylov methods on hard nonlinear equations problems.

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Authors and Affiliations

  1. MCS Division, Argonne National Laboratory, 60439, Argonne, IL, USA

    Ali Bouaricha

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  1. Ali Bouaricha
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Part of this work was performed while the author was research associate at CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique).

Research supported in part by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38.

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Bouaricha, A. Tensor-Krylov methods for large nonlinear equations. Comput Optim Applic 5, 207–232 (1996). https://doi.org/10.1007/BF00248265

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  • DOI: https://doi.org/10.1007/BF00248265

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