Y.Y. Li, K.S. Leung, et al.
J Combin Optim
Monte Carlo matrix trace estimation is a popular randomized technique to estimate the trace of implicitly-defined matrices via averaging quadratic forms across several observations of a random vector. The most common approach to analyze the quality of such estimators is to consider the variance over the total number of observations. In this paper we present a procedure to compute the variance of the estimator proposed in [W. Kong and G. Valiant, Spectrum estimation from samples, Ann. Statist. 45 2017, 5, 2218-2247] for the case of Gaussian random vectors and provide a sharper bound than previously available.
Y.Y. Li, K.S. Leung, et al.
J Combin Optim
Ziv Bar-Yossef, T.S. Jayram, et al.
Journal of Computer and System Sciences
Timothy J. Wiltshire, Joseph P. Kirk, et al.
SPIE Advanced Lithography 1998
Nimrod Megiddo
Journal of Symbolic Computation