Petr Tichý

Vance Faber, Jörg Liesen, Petr Tichý

Given a matrix $A$ and iteration step $k$, we study a best possible attainable upper bound on the GMRES residual norm that does not depend on the initial vector $b$. This quantity is called the worst-case GMRES approximation. We show that the worst case behavior of GMRES for the matrices $A$ and $A^T$ is the same, and we analyze properties of initial vectors for which the worst-case residual norm is attained. In particular, we show that such vectors satisfy a certain "cross equality", and we characterize them as right singular vectors of the corresponding GMRES residual matrix. We show that the worst-case GMRES polynomial may not be uniquely determined, and we consider the relation between the worst-case and the ideal GMRES approximations, giving new examples in which the inequality between the two quantities is sharp at all iteration steps $k\geq 3$. Finally, we give a complete characterization of how the values of the approximation problems in the context of worst-case and ideal GMRES for a real matrix change, when one considers complex (rather than real) polynomials and initial vectors in these problems.

Gérard Meurant, Petr Tichý

In practical conjugate gradient (CG) computations it is important to monitor the quality of the approximate solution to $Ax=b$ so that the CG algorithm can be stopped when the required accuracy is reached. The relevant convergence characteristics, like the $A$-norm of the error or the normwise backward error, cannot be easily computed. However, they can be estimated. Such estimates often depend on approximations of the smallest or largest eigenvalue of~$A$. In the paper we introduce a new upper bound for the $A$-norm of the error, which is closely related to the Gauss-Radau upper bound, and discuss the problem of choosing the parameter $μ$ which should represent a lower bound for the smallest eigenvalue of $A$.The new bound has several practical advantages, the most important one is that it can be used as an approximation to the $A$-norm of the error even if $μ$ is not exactly a lower bound for the smallest eigenvalue of $A$. In this case, $μ$ can be chosen, e.g., as the smallest Ritz value or its approximation. We also describe a very cheap algorithm, based on the incremental norm estimation technique, which allows to estimate the smallest and largest Ritz values during the CG computations. An improvement of the accuracy of these estimates of extreme Ritz values is possible, at the cost of storing the CG coefficients and solving a linear system with a tridiagonal matrix at each CG iteration. Finally, we discuss how to cheaply approximate the normwise backward error. The numerical experiments demonstrate the efficiency of the estimates of the extreme Ritz values, and show their practical use in error estimation in CG.