Multitask kernel-learning parameter prediction method for solving time-dependent linear systems

TL;DR

This paper introduces a data-driven, multitask kernel-learning approach for automatically predicting optimal splitting parameters in matrix splitting methods, significantly improving efficiency in solving large, time-dependent linear systems.

Contribution

It proposes a novel multitask kernel-learning parameter prediction method and a new matrix splitting Kronecker product approach for large-scale time-dependent linear systems.

Findings

Reduces time in selecting splitting parameters compared to existing methods.

Effectively accelerates GMRES when used as a preconditioner.

Achieves speedup ratios of tens to hundreds for large differential Sylvester equations.

Abstract

Matrix splitting iteration methods play a vital role in solving large sparse linear systems. Their performance heavily depends on the splitting parameters, however, the approach of selecting optimal splitting parameters has not been well developed. In this paper, we present a multitask kernel-learning parameter prediction method to automatically obtain relatively optimal splitting parameters, which contains simultaneous multiple parameters prediction and a data-driven kernel learning. For solving time-dependent linear systems, including linear differential systems and linear matrix systems, we give a new matrix splitting Kronecker product method, as well as its convergence analysis and preconditioning strategy. Numerical results illustrate our methods can save an enormous amount of time in selecting the relatively optimal splitting parameters compared with the exists methods. Moreover, our iteration method as a preconditioner can effectively accelerate GMRES. As the dimension of systems increases, all the advantages of our approaches becomes significantly. Especially, for solving the differential Sylvester matrix equation, the speedup ratio can reach tens to hundreds of times when the scale of the system is larger than one hundred thousand.