Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
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.
Sonia Cafieri, Jon Lee, et al.
Journal of Global Optimization
M. Shub, B. Weiss
Ergodic Theory and Dynamical Systems
Alfred K. Wong, Antoinette F. Molless, et al.
SPIE Advanced Lithography 2000
Heng Cao, Haifeng Xi, et al.
WSC 2003