Mixed-Precision Paterson--Stockmeyer Method for Evaluating Polynomials of Matrices

Liu, Xiaobo (2023) Mixed-Precision Paterson--Stockmeyer Method for Evaluating Polynomials of Matrices. [MIMS Preprint]

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Abstract

The Paterson--Stockmeyer method is an evaluation scheme for matrix polynomials with scalar coefficients that arise in many state-of-the-art algorithms based on polynomial or rational approximation, for example, those for computing transcendental matrix functions. We derive a mixed-precision version of the Paterson--Stockmeyer method that is particularly useful for evaluating matrix polynomials with scalar coefficients of decaying magnitude. The key idea is to perform computations on data of small magnitude in low precision, and rounding error analysis is provided for the use of lower-than-working precisions. We focus on the evaluation of the Taylor approximants of the matrix exponential and show the applicability of our method to the existing scaling and squaring algorithms, particularly when the norm of the input matrix (which in practical algorithms is often scaled towards to origin) is sufficiently small. We also demonstrate through experiments the general applicability of our method to the computation of the polynomials from the Pad\'e approximant of the matrix exponential and the Taylor approximant of the matrix cosine. Numerical experiments show our mixed-precision Paterson--Stockmeyer algorithms can be more efficient than its fixed-precision counterpart while delivering the same level of accuracy.

Item Type:	MIMS Preprint
Uncontrolled Keywords:	Paterson--Stockmeyer method, mixed-precision algorithm, rounding error analysis, polynomial evaluation, polynomial of matrices, matrix function
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 15 Linear and multilinear algebra; matrix theory MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis
Divisions:	Manchester Institute for the Mathematical Sciences
Depositing User:	Xiaobo Liu
Date Deposited:	27 Dec 2023 22:12
Last Modified:	27 Dec 2023 22:12
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/2888

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