We consider the problem of estimating the trace and diagonal entries of an N-order tensor (where $N \geq 2$) under the framework where the tensor can only be accessed through tensor-vector multiplication. The aim is to estimate the tensor's diagonal entries and trace by minimizing the number of tensor-vector queries. The seminal work of Hutchinson and its extended version due to Bekas et al. give unbiased estimates of the trace and diagonal elements of a given matrix, respectively, using matrix-vector queries. However, to the best of our knowledge, no analogous results are known for estimating the trace and diagonal entries of higher-order tensors using tensor-vector queries. This paper addresses this gap and presents unbiased estimators for the trace and diagonal entries of a tensor under this model. Our proposed methods can be seen as generalizations of Hutchinson's and Bekas et al.'s estimators and reduce to their estimators when N = 2. We provide a rigorous theoretical analysis of our proposals and complement it with supporting simulations.
翻译:本文研究在仅能通过张量-向量乘法访问张量的框架下,估计N阶张量(其中$N \geq 2$)的迹与对角元的问题。目标是通过最小化张量-向量查询次数来估计张量的对角元与迹。Hutchinson的开创性工作及其由Bekas等人提出的扩展版本分别利用矩阵-向量查询给出了给定矩阵迹与对角元的无偏估计。然而,据我们所知,目前尚无利用张量-向量查询估计高阶张量迹与对角元的类似结果。本文填补了这一空白,在该模型下提出了张量迹与对角元的无偏估计量。我们提出的方法可视为Hutchinson与Bekas等人估计量的推广,当N = 2时即退化为原估计量。我们给出了严格的理论分析,并通过仿真实验加以验证。