How Descriptive are GMRES Convergence Bounds?

09/02/2022
by   Mark Embree, et al.
0

GMRES is a popular Krylov subspace method for solving linear systems of equations involving a general non-Hermitian coefficient matrix. The conventional bounds on GMRES convergence involve polynomial approximation problems in the complex plane. Three popular approaches pose this approximation problem on the spectrum, the field of values, or pseudospectra of the coefficient matrix. We analyze and compare these bounds, illustrating with six examples the success and failure of each. When the matrix departs from normality due only to a low-dimensional invariant subspace, we discuss how these bounds can be adapted to exploit this structure. Since the Arnoldi process that underpins GMRES provides approximations to the pseudospectra, one can estimate the GMRES convergence bounds as an iteration proceeds.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/17/2021

A New Modified Newton-Type Iteration Method for Solving Generalized Absolute Value Equations

For the large sparse generalized absolute value equations (GAVEs) , the ...
research
03/16/2022

Convergence Acceleration of Preconditioned CG Solver Based on Error Vector Sampling for a Sequence of Linear Systems

In this paper, we focus on solving a sequence of linear systems with an ...
research
12/21/2019

Research on Clustering Performance of Sparse Subspace Clustering

Recently, sparse subspace clustering has been a valid tool to deal with ...
research
07/25/2021

Intrusive and Non-Intrusive Polynomial Chaos Approximations for a Two-Dimensional Steady State Navier-Stokes System with Random Forcing

While convergence of polynomial chaos approximation for linear equations...
research
12/19/2022

A defect-correction algorithm for quadratic matrix equations, with applications to quasi-Toeplitz matrices

A defect correction formula for quadratic matrix equations of the kind A...
research
12/15/2022

Convergence of the Eberlein diagonalization method under the generalized serial pivot strategies

The Eberlein method is a Jacobi-type process for solving the eigenvalue ...
research
02/25/2020

Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors

We study the linear subspace fitting problem in the overparameterized se...

Please sign up or login with your details

Forgot password? Click here to reset