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Higher order fluctuations of extremal eigenvalues of sparse random matrices

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abstract

We consider extremal eigenvalues of sparse random matrices, a class of random matrices including the adjacency matrices of Erd\H{o}s-R\'{e}nyi graphs $\mathcal{G}(N,p)$. Recently, it was shown that the leading order fluctuations of extremal eigenvalues are given by a single random variable associated with the total degree of the graph (Ann. Probab., 48(2):916-962, 2020; Probab. Theory Related Fields, 180:985-1056, 2021). We construct a sequence of random correction terms to capture higher (sub-leading) order fluctuations of extremal eigenvalues in the regime $N^{\epsilon} < pN < N^{1/3-\epsilon}$. Using these random correction terms, we prove a local law up to a shifted edge and recover the rigidity of extremal eigenvalues under some corrections for $pN>N^{\epsilon}$.

fields

stat.ML 1

years

2024 1

verdicts

REJECT 1

representative citing papers

Matrix Completion via Residual Spectral Matching

stat.ML · 2024-12-13 · reject · novelty 7.0

A residual spectral matching estimator for noisy matrix completion matches the singular values of residuals to those of sparse random matrices and claims minimax optimal error rates.

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  • Matrix Completion via Residual Spectral Matching stat.ML · 2024-12-13 · reject · none · ref 1016 · internal anchor

    A residual spectral matching estimator for noisy matrix completion matches the singular values of residuals to those of sparse random matrices and claims minimax optimal error rates.