A CUR Krylov Solver for Large-Scale Linear Matrix Equations

Fuente: arXiv
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Auteurs principaux: Akbari, Saeed, Lombardi, Damiano, Babaee, Hessam
Format: Preprint
Publié: 2025
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author Akbari, Saeed
Lombardi, Damiano
Babaee, Hessam
author_facet Akbari, Saeed
Lombardi, Damiano
Babaee, Hessam
contents Developing efficient solvers for large-scale multi-term linear matrix equations remains a central challenge in numerical linear algebra and is still largely unresolved. This paper introduces a methodology leveraging CUR decomposition for solving large-scale generalized Sylvester as well as non-Sylvester multi-term equations on low-rank matrix manifolds. The approach decomposes the original equation into two smaller subproblems: one involving all columns with a small subset of rows, and the other involving all rows with a small subset of columns. The rows and columns are strategically selected using the discrete empirical interpolation method. We further utilize the CUR properties and propose a novel iterative scheme that removes the dependencies between selected and unselected rows (and likewise for columns), thereby enabling the subset problems to be solved independently. We present a Krylov-based scheme for solving the resulting subproblems, which scales effectively to large problems and does not rely on a Sylvester structure. The method incorporates rank adaptivity, dynamically adjusting computational rank to reach the desired accuracy. The methodology is demonstrated in three representative settings: (i) implicit time integration of matrix differential equations on low-rank manifolds, leading to multi-term linear matrix equations; (ii) large-scale steady-state generalized Lyapunov equations including cases of size up to $10^{13}$ unknown entries; and (iii) non-Sylvester linear matrix equations with Hadamard product terms, such as those arising in nonlinear partial differential equations.
format Preprint
id arxiv_https___arxiv_org_abs_2511_14015
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A CUR Krylov Solver for Large-Scale Linear Matrix Equations
Akbari, Saeed
Lombardi, Damiano
Babaee, Hessam
Numerical Analysis
Developing efficient solvers for large-scale multi-term linear matrix equations remains a central challenge in numerical linear algebra and is still largely unresolved. This paper introduces a methodology leveraging CUR decomposition for solving large-scale generalized Sylvester as well as non-Sylvester multi-term equations on low-rank matrix manifolds. The approach decomposes the original equation into two smaller subproblems: one involving all columns with a small subset of rows, and the other involving all rows with a small subset of columns. The rows and columns are strategically selected using the discrete empirical interpolation method. We further utilize the CUR properties and propose a novel iterative scheme that removes the dependencies between selected and unselected rows (and likewise for columns), thereby enabling the subset problems to be solved independently. We present a Krylov-based scheme for solving the resulting subproblems, which scales effectively to large problems and does not rely on a Sylvester structure. The method incorporates rank adaptivity, dynamically adjusting computational rank to reach the desired accuracy. The methodology is demonstrated in three representative settings: (i) implicit time integration of matrix differential equations on low-rank manifolds, leading to multi-term linear matrix equations; (ii) large-scale steady-state generalized Lyapunov equations including cases of size up to $10^{13}$ unknown entries; and (iii) non-Sylvester linear matrix equations with Hadamard product terms, such as those arising in nonlinear partial differential equations.
title A CUR Krylov Solver for Large-Scale Linear Matrix Equations
topic Numerical Analysis
url https://arxiv.org/abs/2511.14015