Analysis of stochastic probing methods for estimating the trace of functions of sparse symmetric matrices

TL;DR

This paper provides a detailed analysis of stochastic probing methods for estimating the trace of matrix functions, revealing conditions where they outperform deterministic methods and demonstrating their effectiveness through numerical experiments.

Contribution

It offers the first thorough theoretical analysis of stochastic probing techniques, identifying when they outperform deterministic approaches and extending previous results on single-vector stochastic estimators.

Findings

Stochastic probing can have lower expected error than deterministic methods under certain conditions.

Using a single stochastic vector can be better than deterministic probing in some cases.

Numerical experiments confirm the theoretical advantages of stochastic probing methods.

Abstract

We consider the problem of estimating the trace of a matrix function $f(A)$. In certain situations, in particular if $f(A)$ cannot be well approximated by a low-rank matrix, combining probing methods based on graph colorings with stochastic trace estimation techniques can yield accurate approximations at moderate cost. So far, such methods have not been thoroughly analyzed, though, but were rather used as efficient heuristics by practitioners. In this manuscript, we perform a detailed analysis of stochastic probing methods and, in particular, expose conditions under which the expected approximation error in the stochastic probing method scales more favorably with the dimension of the matrix than the error in non-stochastic probing. Extending results from [E. Aune, D. P. Simpson, J. Eidsvik, Parameter estimation in high dimensional Gaussian distributions, Stat. Comput., 24, pp. 247--263, 2014], we also characterize situations in which using just one stochastic vector is always -- not only in expectation -- better than the deterministic probing method. Several numerical experiments illustrate our theory and compare with existing methods.