Volume 60, pp. 381-404, 2024.

Polynomial preconditioning for the action of the matrix square root and inverse square root

Andreas Frommer, Gustavo Ramirez-Hidalgo, Marcel Schweitzer, and Manuel Tsolakis


While preconditioning is a long-standing concept to accelerate iterative methods for linear systems, generalizations to matrix functions are still in their infancy. We go a further step in this direction, introducing polynomial preconditioning for Krylov subspace methods that approximate the action of the matrix square root and inverse square root on a vector. Preconditioning reduces the subspace size and therefore avoids the storage problem together with—for non-Hermitian matrices—the increased computational cost per iteration that arises in the unpreconditioned case. Polynomial preconditioning is an attractive alternative to current restarting or sketching approaches since it is simpler and computationally more efficient. We demonstrate this for several numerical examples.

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Key words

polynomial preconditioning, matrix square root, inverse square root, Krylov space, matrix functions

AMS subject classifications

65F60, 65F08, 65F50, 15A16

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