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We consider generalizations of the Sylvester matrix equation, consisting of the sum of a Sylvester operator and a linear operator $\Pi$ with a particular structure. More precisely, the commutator of the matrix coefficients of the operator…

Numerical Analysis · Mathematics 2019-06-18 Elias Jarlebring , Giampaolo Mele , Davide Palitta , Emil Ringh

We propose a fast collocation method based on Krylov subspace iterative solver on general nonuniform grids for the fractional Laplacian problem, in which the fractional operator is presented in a singular integral formulation. The method is…

Numerical Analysis · Mathematics 2025-11-13 Meijie Kong , Hongfei Fu

It is well known that for general linear systems, only optimal Krylov methods with long recurrences exist. For special classes of linear systems it is possible to find optimal Krylov methods with short recurrences. In this paper we consider…

Numerical Analysis · Mathematics 2023-04-11 R. Idema , C. Vuik

Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited…

For many applications involving a sequence of linear systems with slowly changing system matrices, subspace recycling, which exploits relationships among systems and reuses search space information, can achieve huge gains in iterations…

Numerical Analysis · Mathematics 2023-06-28 Misha E. Kilmer , Eric de Sturler

Krylov subspace methods for solving linear systems of equations involving skew-symmetric matrices have gained recent attention. Numerical equivalences among Krylov subspace methods for nonsingular skew-symmetric linear systems have been…

Numerical Analysis · Mathematics 2023-08-01 Kui Du , Jia-Jun Fan , Xiao-Hui Sun , Fang Wang , Ya-Lan Zhang

A finite element methodology for large classes of variational boundary value problems is defined which involves discretizing two linear operators: (1) the differential operator defining the spatial boundary value problem; and (2) a Riesz…

Numerical Analysis · Mathematics 2017-12-08 Brendan Keith , Socratis Petrides , Federico Fuentes , Leszek Demkowicz

Block Krylov subspace methods (KSMs) comprise building blocks in many state-of-the-art solvers for large-scale matrix equations as they arise, e.g., from the discretization of partial differential equations. While extended and rational…

Numerical Analysis · Mathematics 2020-02-06 Daniel Kressner , Kathryn Lund , Stefano Massei , Davide Palitta

In this paper, we present a finite element method (FEM) framework enhanced by an operator-adapted wavelet decomposition algorithm designed for the efficient analysis of multiscale electromagnetic problems. Usual adaptive FEM approaches,…

Computational Physics · Physics 2026-02-18 F. Şık , F. L. Teixeira , B. Shanker

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…

Numerical Analysis · Mathematics 2025-11-19 Saeed Akbari , Damiano Lombardi , Hessam Babaee

The rational Krylov subspace method (RKSM) and the low-rank alternating directions implicit (LR-ADI) iteration are established numerical tools for computing low-rank solution factors of large-scale Lyapunov equations. In order to generate…

Numerical Analysis · Mathematics 2019-05-07 Patrick Kürschner , Melina A. Freitag

In this paper, we present a methodology for stability analysis of a general class of systems defined by coupled Partial Differential Equations (PDEs) with spatially dependent coefficients and a general class of boundary conditions. This…

Optimization and Control · Mathematics 2016-03-28 Evgeny Meyer , Matthew M. Peet

Parametric linear systems are linear systems of equations in which some symbolic parameters, that is, symbols that are not considered to be candidates for elimination or solution in the course of analyzing the problem, appear in the…

Rings and Algebras · Mathematics 2025-09-01 Robert M. Corless , Mark Giesbrecht , Leili Rafiee Sevyeri , B. David Saunders

In this article, we present a parallel recursive algorithm based on multi-level domain decomposition that can be used as a precondtioner to a Krylov subspace method to solve sparse linear systems of equations arising from the discretization…

Numerical Analysis · Mathematics 2012-10-24 Rahul S. Sampath , Bobby Philip , Srikanth Allu , Srdjan Simunovic

We investigate the application of Krylov space methods to the solution of shifted linear systems of the form (A+\sigma) x - b = 0 for several values of \sigma simultaneously, using only as many matrix-vector operations as the solution of a…

High Energy Physics - Lattice · Physics 2007-05-23 B. Jegerlehner

An efficient and robust linear scaling method is presented for large scale {\it ab initio} electronic structure calculations of a wide variety of materials including metals. The detailed short range and the effective long range…

Other Condensed Matter · Physics 2016-08-31 Taisuke Ozaki

We provide convergence rates for Krylov subspace solutions to the trust-region and cubic-regularized (nonconvex) quadratic problems. Such solutions may be efficiently computed by the Lanczos method and have long been used in practice. We…

Optimization and Control · Mathematics 2019-01-03 Yair Carmon , John C. Duchi

The discretization of the double-layer potential integral equation for the interior Dirichlet Laplace problem in a domain with smooth boundary results in a linear system that has a bounded condition number. Thus, the number of iterations…

Numerical Analysis · Mathematics 2014-02-27 Bryan Quaife , George Biros

An algorithm for constructing a $J$-orthogonal basis of the extended Krylov subspace $\mathcal{K}_{r,s}=\operatorname{range}\{u,Hu, H^2u,$ $ \ldots, $ $H^{2r-1}u, H^{-1}u, H^{-2}u, \ldots, H^{-2s}u\},$ where $H \in \mathbb{R}^{2n \times…

Numerical Analysis · Mathematics 2022-02-28 Peter Benner , Heike Faßbender , Michel-Niklas Senn

Mixed-effects models are widely used to model data with hierarchical grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed random effects, current standard computations relying on…

Methodology · Statistics 2026-05-15 Pascal Kündig , Fabio Sigrist