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Top eigenpair statistics of diluted Wishart matrices

Budnick, Barak; Forer, Preben; Vivo, Pierpaolo; Aufiero, Sabrina; Bartolucci, Silvia; Caccioli, Fabio; (2025) Top eigenpair statistics of diluted Wishart matrices. Journal of Physics A: Mathematical and Theoretical , 58 (32) , Article 325001. 10.1088/1751-8121/add821. Green open access

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Abstract

Using the replica method, we compute the statistics of the top eigenpair of diluted covariance matrices of the form $\bm J = \bm X^T \bm X$, where $\bm X$ is a $N\times M$ sparse data matrix, in the limit of large $N,M$ with fixed ratio and a bounded number of nonzero entries. We allow for random non-zero weights, provided they lead to an isolated largest eigenvalue. By formulating the problem as the optimisation of a quadratic Hamiltonian constrained to the $N$-sphere at low temperatures, we derive a set of recursive distributional equations for auxiliary probability density functions, which can be efficiently solved using a population dynamics algorithm. The average largest eigenvalue is identified with a Lagrange parameter that governs the convergence of the algorithm, and the resulting stable populations are then used to evaluate the density of the top eigenvector's components. We find excellent agreement between our analytical results and numerical results obtained from direct diagonalisation.

Type: Article
Title: Top eigenpair statistics of diluted Wishart matrices
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1751-8121/add821
Publisher version: https://doi.org/10.1088/1751-8121/add821
Language: English
Additional information: Copyright © 2025 The Author(s). Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10211567
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