A Posteriori Error Estimate for Computing $\mathrm{tr}(f(A))$ by Using the Lanczos Method
Jie Chen, Yousef Saad

TL;DR
This paper introduces reliable a posteriori error estimates for computing the trace of a matrix function using the Lanczos method, addressing a key challenge in Krylov subspace methods for large matrices.
Contribution
It proposes novel error estimates for both bilinear forms and the trace of matrix functions, improving accuracy assessment in Lanczos-based computations.
Findings
Error estimates accurately predict the number of correct digits.
Trace error estimate effectively guides stopping criteria.
Application to Gaussian processes demonstrates practical utility.
Abstract
An outstanding problem when computing a function of a matrix, , 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 itself, but rather, matrix-vector products or bilinear forms. When the computation related to 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 for a symmetric positive-definite matrix by using the Lanczos method and make two contributions: (i) we propose an error estimate for the bilinear form…
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Taxonomy
TopicsNumerical Methods and Algorithms · Matrix Theory and Algorithms · Mathematical Approximation and Integration
