On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic
Provides guidance for testing new CG variants on rounding-error-sensitive problems, relevant to numerical linear algebra practitioners.
The paper analyzes three mathematically equivalent conjugate gradient variants in finite precision, showing that their convergence behavior correlates with how well they satisfy conditions for perturbed CG theory. For sensitive problems, variants differ significantly; for robust problems, they converge similarly until ultimate accuracy.
We consider three mathematically equivalent variants of the conjugate gradient (CG) algorithm and how they perform in finite precision arithmetic. It was shown in [{\em Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences}, Lin.~Alg.~Appl., 113 (1989), pp.~7-63] that under certain conditions the convergence of a slightly perturbed CG computation is like that of exact CG for a matrix with many eigenvalues distributed throughout tiny intervals about the eigenvalues of the given matrix, the size of the intervals being determined by how closely these conditions are satisfied. We determine to what extent each of these variants satisfies the desired conditions, using a set of test problems and show that there is significant correlation between how well these conditions are satisfied and how well the finite precision computation converges before reaching its ultimately attainable accuracy. We show that for problems where the width of the intervals containing the eigenvalues of the associated exact CG matrix makes a significant difference in the behavior of exact CG, the different CG variants behave differently in finite precision arithmetic. For problems where the interval width makes little difference or where the convergence of exact CG is essentially governed by the upper bound based on the square root of the condition number of the matrix, the different CG variants converge similarly in finite precision arithmetic until the ultimate level of accuracy is achieved, although this ultimate level of accuracy may be different for the different variants. This points to the need for testing new CG variants on problems that are especially sensitive to rounding errors.