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 by Kong and Valiant [Ann. Statist. 45 (5), pp. 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
Martin Charles Golumbic, Renu C. Laskar
Discrete Applied Mathematics
Salvatore Certo, Anh Pham, et al.
Quantum Machine Intelligence
Fernando Martinez, Juntao Chen, et al.
AAAI 2025