AbstractIn this paper we propose a general approach by which eigenvalues with a special property of a given matrix A can be obtained. In this approach we first determine a scalar function ψ: C → C whose modulus is maximized by the eigenvalues that have the special property. Next, we compute the generalized power iterations uinj + 1 = ψ(A)uj, j = 0, 1,…, where u0 is an arbitrary initial vector. Finally, we apply known Krylov subspace methods, such as the Arnoldi and Lanczos methods, to the vector un for some sufficiently large n. We can also apply the simultaneous iteration method to the subspace span{x(n)1,…,x(n)k} with some sufficiently large n, where x(j+1)m = ψ(A)x(j)m, j = 0, 1,…, m = 1,…, k. In all cases the resulting Ritz pairs are ap...
AbstractWe discuss a class of deflated block Krylov subspace methods for solving large scale matrix ...
We provide a general framework for the understanding of Inexact Krylov subspace methods for the solu...
AbstractAlgorithms to solve large sparse eigenvalue problems are considered. A new class of algorith...
Krylov subspace methods for eigenvalues with special properties and their analysis for normal matric...
Abstract. Krylov subspace methods are strongly related to polynomial spaces and their convergence an...
We consider solving eigenvalue problems or model reduction problems for a quadratic matrix polynomia...
AbstractThe problem of approximation of an eigenpair of a large n × n matrix A is considered. We stu...
AbstractWe consider solving eigenvalue problems or model reduction problems for a quadratic matrix p...
We describe an indefinite state of Arnoldi’s method for solving the eigenvalues problems. In the fol...
We consider solving eigenvalue problems or model reduction problems for a quadratic matrix polynomia...
This diploma thesis from 2006 reviews various definitions of matrix functions and polynomial Krylov ...
This talk is about the solution of non-linear eigenvalue problems and linear systems with a nonlinea...
In most practical cases, the convergence of the GMRES method applied to a linear algebraic system Ax...
Title: Krylov Subspace Methods - Analysis and Application Author: Tomáš Gergelits Department: Depart...
We describe a generalization of the compact rational Krylov (CORK) methods for polynomial and ration...
AbstractWe discuss a class of deflated block Krylov subspace methods for solving large scale matrix ...
We provide a general framework for the understanding of Inexact Krylov subspace methods for the solu...
AbstractAlgorithms to solve large sparse eigenvalue problems are considered. A new class of algorith...
Krylov subspace methods for eigenvalues with special properties and their analysis for normal matric...
Abstract. Krylov subspace methods are strongly related to polynomial spaces and their convergence an...
We consider solving eigenvalue problems or model reduction problems for a quadratic matrix polynomia...
AbstractThe problem of approximation of an eigenpair of a large n × n matrix A is considered. We stu...
AbstractWe consider solving eigenvalue problems or model reduction problems for a quadratic matrix p...
We describe an indefinite state of Arnoldi’s method for solving the eigenvalues problems. In the fol...
We consider solving eigenvalue problems or model reduction problems for a quadratic matrix polynomia...
This diploma thesis from 2006 reviews various definitions of matrix functions and polynomial Krylov ...
This talk is about the solution of non-linear eigenvalue problems and linear systems with a nonlinea...
In most practical cases, the convergence of the GMRES method applied to a linear algebraic system Ax...
Title: Krylov Subspace Methods - Analysis and Application Author: Tomáš Gergelits Department: Depart...
We describe a generalization of the compact rational Krylov (CORK) methods for polynomial and ration...
AbstractWe discuss a class of deflated block Krylov subspace methods for solving large scale matrix ...
We provide a general framework for the understanding of Inexact Krylov subspace methods for the solu...
AbstractAlgorithms to solve large sparse eigenvalue problems are considered. A new class of algorith...