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On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic
SIAM Journal on Scientific Computing  (IF2.373),  Pub Date : 2021-07-15, DOI: 10.1137/20m1346249
Anne Greenbaum, Hexuan Liu, Tyler Chen

SIAM Journal on Scientific Computing, Ahead of Print.
We consider three mathematically equivalent variants of the conjugate gradient (CG) algorithm and how they perform in finite precision arithmetic. It was shown in [Greenbaum, Lin. Alg. Appl., 113 (1989), pp. 7--63] that under certain conditions involving local orthogonality and approximate satisfaction of a recurrence formula, that may be satisfied by a finite precision CG computation, the convergence of that computation is like that of exact CG for a matrix with many eigenvalues distributed throughout tiny intervals about the eigenvalues of the given matrix. 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.