August 2022 Hamilton–Jacobi equations for nonsymmetric matrix inference
Hong-Bin Chen
Author Affiliations +
Ann. Appl. Probab. 32(4): 2540-2567 (August 2022). DOI: 10.1214/21-AAP1739

Abstract

We study the high-dimensional limit of the free energy associated with the inference problem of a rank-one nonsymmetric matrix. The matrix is expressed as the outer product of two vectors, not necessarily independent. The distributions of the two vectors are only assumed to have scaled bounded supports. We bound the difference between the free energy and the solution to a suitable Hamilton–Jacobi equation in terms of two much simpler quantities: concentration rate of this free energy, and the convergence rate of a simpler free energy in a decoupled system. To demonstrate the versatility of this approach, we apply our result to the i.i.d. case and the spherical case. By plugging in estimates of the two simpler quantities, we identify the limits and obtain convergence rates.

Acknowledgments

I am grateful to Jean-Christophe Mourrat for introducing me to this subject and for many helpful insights. I would like to thank Jiaming Xia for helpful comments.

Citation

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Hong-Bin Chen. "Hamilton–Jacobi equations for nonsymmetric matrix inference." Ann. Appl. Probab. 32 (4) 2540 - 2567, August 2022. https://doi.org/10.1214/21-AAP1739

Information

Received: 1 June 2020; Revised: 1 March 2021; Published: August 2022
First available in Project Euclid: 17 August 2022

MathSciNet: MR4474513
zbMATH: 1498.82010
Digital Object Identifier: 10.1214/21-AAP1739

Subjects:
Primary: 82B44 , 82D30

Keywords: Free energy , Hamilton–Jacobi equation , nonsymmetric matrix , statistical inference

Rights: Copyright © 2022 Institute of Mathematical Statistics

Vol.32 • No. 4 • August 2022
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