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Bounds on the covariance matrix of the Sherrington–Kirkpatrick model

Publication ,  Journal Article
El Alaoui, A; Gaitonde, J
Published in: Electronic Communications in Probability
January 1, 2024

We consider the Sherrington-Kirkpatrick model with no external field and inverse temperature β < 1 and prove that the expected operator norm of the covariance matrix of the Gibbs measure is bounded by a constant depending only on β. This answers an open question raised by Talagrand, who proved a bound of C(β)(log n)8. Our result follows by establishing an approximate formula for the covariance matrix which we obtain by differentiating the TAP equations and then optimally controlling the associated error terms. We complement this result by showing diverging lower bounds on the operator norm, both at the critical and low temperatures.

Duke Scholars

Published In

Electronic Communications in Probability

DOI

EISSN

1083-589X

Publication Date

January 1, 2024

Volume

29

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics
 

Citation

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El Alaoui, A., & Gaitonde, J. (2024). Bounds on the covariance matrix of the Sherrington–Kirkpatrick model. Electronic Communications in Probability, 29. https://doi.org/10.1214/24-ECP582
El Alaoui, A., and J. Gaitonde. “Bounds on the covariance matrix of the Sherrington–Kirkpatrick model.” Electronic Communications in Probability 29 (January 1, 2024). https://doi.org/10.1214/24-ECP582.
El Alaoui A, Gaitonde J. Bounds on the covariance matrix of the Sherrington–Kirkpatrick model. Electronic Communications in Probability. 2024 Jan 1;29.
El Alaoui, A., and J. Gaitonde. “Bounds on the covariance matrix of the Sherrington–Kirkpatrick model.” Electronic Communications in Probability, vol. 29, Jan. 2024. Scopus, doi:10.1214/24-ECP582.
El Alaoui A, Gaitonde J. Bounds on the covariance matrix of the Sherrington–Kirkpatrick model. Electronic Communications in Probability. 2024 Jan 1;29.

Published In

Electronic Communications in Probability

DOI

EISSN

1083-589X

Publication Date

January 1, 2024

Volume

29

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics