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• W2404492143 abstract "Methods for the polynomial eigenvalue problem sometimes need to be followed by an iterative refinement process to improve the accuracy of the computed solutions. This can be accomplished by means of a Newton iteration tailored to matrix polynomials. The computational cost of this step is usually higher than the cost of computing the initial approximations, due to the need of solving multiple linear systems of equations with a bordered coefficient matrix. An effective parallelization is thus important, and we propose different approaches for the message-passing scenario. Some schemes use a subcommunicator strategy in order to improve the scalability whenever direct linear solvers are used. We show performance results for the various alternatives implemented in the context of SLEPc, the Scalable Library for Eigenvalue Problem Computations. Copyright © 2016 John Wiley & Sons, Ltd." @default.
• W2404492143 created "2016-06-24" @default.
• W2404492143 creator A5032159763 @default.
• W2404492143 creator A5061625543 @default.
• W2404492143 date "2016-05-19" @default.
• W2404492143 modified "2023-09-27" @default.
• W2404492143 title "Parallel iterative refinement in polynomial eigenvalue problems" @default.
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• W2404492143 doi "https://doi.org/10.1002/nla.2052" @default.
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