Results 171 to 180 of about 81,041 (236)

The Double-Layer Potential for Spectral Constants Revisited. [PDF]

Integr Equ Oper Theory
Schwenninger FL, de Vries J.
europepmc   +1 more source

Model reduction of structural mechanical response in the time domain. [PDF]

Sci Rep
Yan X, Guo X, He N, Shi J, Zhao D.
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Achieving high precision in analog in-memory computing systems. [PDF]

Npj Unconv Comput
Mannocci P, Larelli G, Bonomi M, Ielmini D.   +3 more
europepmc   +1 more source

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On tensor tubal-Krylov subspace methods

Linear and Multilinear Algebra, 2021
In this paper, we will introduce some new tubal-Krylov subspace methods for solving linear tensor equations. Using the well known tensor T-product, we will in particular define the tensor tubal-global GMRES that could be seen as a generalization of the ...
A. El ichi, K. Jbilou, R. Sadaka
semanticscholar   +3 more sources

Analysis of Augmented Krylov Subspace Methods

SIAM Journal on Matrix Analysis and Applications, 1997
``Augmented Krylov methods'' are studied theoretically. These methods for solving a linear system are projection methods in which the subspace of projection is of the form \(K = K_{m} + W\), where \(K_{m}\) is the standard Krylov subspace, which is augmented by another subspace \(W\). The subspace \(W\) can be chosen in different ways.
Yousef Saad
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Truncation Strategies for Optimal Krylov Subspace Methods

SIAM Journal on Numerical Analysis, 1999
Summary: Optimal Krylov subspace methods like GMRES and GCR have to compute an orthogonal basis for the entire Krylov subspace to compute the minimal residual approximation to the solution. Therefore, when the number of iterations becomes large, the amount of work and the storage requirements become excessive. In practice one has to limit the resources.
Eric de Sturler
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Convergence analysis of Krylov subspace methods

GAMM-Mitteilungen, 2004
AbstractOne of the most powerful tools for solving large and sparse systems of linear algebraic equations is a class of iterative methods called Krylov subspace methods. Their significant advantages like low memory requirements and good approximation properties make them very popular, and they are widely used in applications throughout science and ...
Liesen, Jörg, Tichý, Petr
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Chapter 6: Krylov Subspace Methods

Matrix Analysis and Computations, 2021

semanticscholar   +2 more sources

Krylov Subspace Methods for Linear Systems

Springer Series in Computational Mathematics, 2022
T. Sogabe
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