PreprintCC BY 4.0arXiv
Sampling from the Sherrington-Kirkpatrick Model up to Criticality
Abstract
We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature β< 1 to an arbitrary inverse polynomial accuracy in total variation.
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BibTeX
@misc{du2026sampling,
title = {{Sampling from the Sherrington-Kirkpatrick Model up to Criticality}},
author = {Hang Du and Brice Huang and Sidhanth Mohanty and Amit Rajaraman},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.17717},
eprint = {2610.17717},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.17717},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Sampling from the Sherrington-Kirkpatrick Model up to Criticality
AU - Hang Du
AU - Brice Huang
AU - Sidhanth Mohanty
AU - Amit Rajaraman
PY - 2026
DA - 2026/10/13
PB - arXiv
DO - 10.48550/arxiv.2610.17717
UR - https://arxiv.org/abs/2610.17717
AB - We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature β< 1 to an arbitrary inverse polynomial accuracy in total variation.
ER -