Improving stochastic estimates with inference methods: calculating matrix diagonals

TL;DR

This paper introduces a statistical inference-based method, utilizing the generalized Wiener filter, to efficiently estimate matrix diagonals from limited probes, significantly reducing computational costs in applications like image reconstruction.

Contribution

It develops a novel approach combining inference methods with matrix probing to improve accuracy and efficiency in diagonal estimation from few samples.

Findings

Achieves 2 to 10 times speedup in diagonal estimation

Effective with limited computational probes

Applicable to real-world and simulated problems

Abstract

Estimating the diagonal entries of a matrix, that is not directly accessible but only available as a linear operator in the form of a computer routine, is a common necessity in many computational applications, especially in image reconstruction and statistical inference. Here, methods of statistical inference are used to improve the accuracy or the computational costs of matrix probing methods to estimate matrix diagonals. In particular, the generalized Wiener filter methodology, as developed within information field theory, is shown to significantly improve estimates based on only a few sampling probes, in cases in which some form of continuity of the solution can be assumed. The strength, length scale, and precise functional form of the exploited autocorrelation function of the matrix diagonal is determined from the probes themselves. The developed algorithm is successfully applied to mock and real world problems. These performance tests show that, in situations where a matrix diagonal has to be calculated from only a small number of computationally expensive probes, a speedup by a factor of 2 to 10 is possible with the proposed method.