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Tokyo Probability Seminar
Seminar information archive ～05/02｜Next seminar｜Future seminars 05/03～
| Date, time & place | Monday 16:00 - 17:30 126Room #126 (Graduate School of Math. Sci. Bldg.) |
|---|---|
| Organizer(s) | Makiko Sasada, Shuta Nakajima, Masato Hoshino |
2024/12/16
16:00-17:30 Room #126 (Graduate School of Math. Sci. Bldg.)
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Shu Kanazawa (Kyoto University)
Central limit theorem for linear eigenvalue statistics of the adjacency matrices of random simplicial complexes
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Shu Kanazawa (Kyoto University)
Central limit theorem for linear eigenvalue statistics of the adjacency matrices of random simplicial complexes
[ Abstract ]
We consider the (higher-dimensional) adjacency matrix of the Linial-Meshulam complex model, which is a higher-dimensional generalization of the Erdős-Rényi random graph model. Recently, Knowles and Rosenthal proved that the empirical spectral distribution
of the adjacency matrix is asymptotically given by Wigner's semicircle law in a diluted regime. In this talk, I will present a central limit theorem for the linear eigenvalue statistics for test functions of polynomial growth that is of class C2 on a closed
interval. The proof is based on higher-dimensional combinatorial enumerations and concentration properties of random symmetric matrices. Furthermore, when the test function is a polynomial function, we obtain the explicit formula for the variance of the limiting
Gaussian distribution. This is joint work with Khanh Duy Trinh (Waseda University).
We consider the (higher-dimensional) adjacency matrix of the Linial-Meshulam complex model, which is a higher-dimensional generalization of the Erdős-Rényi random graph model. Recently, Knowles and Rosenthal proved that the empirical spectral distribution
of the adjacency matrix is asymptotically given by Wigner's semicircle law in a diluted regime. In this talk, I will present a central limit theorem for the linear eigenvalue statistics for test functions of polynomial growth that is of class C2 on a closed
interval. The proof is based on higher-dimensional combinatorial enumerations and concentration properties of random symmetric matrices. Furthermore, when the test function is a polynomial function, we obtain the explicit formula for the variance of the limiting
Gaussian distribution. This is joint work with Khanh Duy Trinh (Waseda University).
