Year: 2025
Type: preprint
Abstract: This paper studies two potential modifications of XTrace (Epperly et al., SIMAX 45(1):1-23, 2024), a randomized algorithm for estimating the trace of a matrix. The first is a variance reduction step that averages the output of XTrace over right-multiplications of the test vectors by random orthogonal matrices. The second is to form a low-rank approximation to the matrix using the whole Krylov space produced by the test vectors, rather than the output of a single power iteration as is used by XTrace. Experiments on synthetic data show that the first modification offers only slight benefits in practice, while the second can lead to significant improvements depending on the spectrum of the matrix.
Source: arXiv (Cornell University)
Author: Hallman, Eric
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417005277
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