An outstanding problem when computing a function of a matrix, f(A), by using a Krylov method is to accurately estimate errors when convergence is slow. Apart from the case of the exponential function which has been extensively studied in the past, there are no well-established solutions to the problem. Often the quantity of interest in applications is not the matrix f(A) itself, but rather, matrix-vector products or bilinear forms. When the computation related to f(A) is a building block of a larger problem (e.g., approximately computing its trace), a consequence of the lack of reliable error estimates is that the accuracy of the computed result is unknown. In this paper, we consider the problem of computing tr(f(A)) for a symmetric positive-definite matrix A by using the Lanczos method and make two contributions: (i) we propose an error estimate for the bilinear form associated with f(A), and (ii) an error estimate for the trace of f(A). We demonstrate the practical usefulness of these estimates for large, sparse or structured, matrices and in particular, show that the trace error estimate is indicative of the number of accurate digits. As an application, we compute the log-determinant of a covariance matrix in Gaussian process analysis and underline the importance of error tolerance as a stopping criterion, as a means of bounding the number of Lanczos steps to achieve a desired accuracy.

By:* Jie Chen, Yousef Saad*

Published in: RC25650 in 2017

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