Limited‐memory polynomial methods for large‐scale matrix functions

doi:10.1002/gamm.202000019

Limited‐memory polynomial methods for large‐scale matrix functions

2020-09-10

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DOI：10.1002/gamm.202000019

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AbstractMatrix functions are a central topic of linear algebra, and problems requiring their numerical approximation appear increasingly often in scientific computing. We review various limited‐memory methods for the approximation of the action of a large‐scale matrix function on a vector. Emphasis is put on polynomial methods, whose memory requirements are known or prescribed a priori. Methods based on explicit polynomial approximation or interpolation, as well as restarted Arnoldi methods, are treated in detail. An overview of existing software is also given, as well as a discussion of challenging open problems.

Limited‐memory polynomial methods for large‐scale matrix functions

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