Improving stochastic estimates with inference methods: calculating matrix diagonals

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.

9 pages, 6 figures, accepted by Phys. Rev. E; introduction revised, results unchanged; page proofs implemented

References in corpus (3)

• Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty
• Reconstructing signals from noisy data with unknown signal and noise covariance
• Probing magnetic helicity with synchrotron radiation and Faraday rotation

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• D${}^3$PO - Denoising, Deconvolving, and Decomposing Photon Observations
• Information Field Theory and Artificial Intelligence
• Noisy independent component analysis of auto-correlated components
• Stochastic determination of matrix determinants