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arXiv 2212.02445math.PRmath-phmath.MP
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Sherrington-Kirkpatrick模型协方差矩阵的界

Bounds on the covariance matrix of the Sherrington-Kirkpatrick model

Ahmed El Alaoui, Jason Gaitonde

AI总结:

本文通过微分TAP方程建立近似公式并控制误差项,证明无外场且逆温度$β<1$的Sherrington-Kirkpatrick模型Gibbs测度协方差矩阵期望算子范数被仅依赖于$β$的常数界定,回答了Talagrand的开放问题,并给出临界与低温下发散下界。

摘要:

我们考虑无外场且逆温度$β<1$的Sherrington-Kirkpatrick模型,证明Gibbs测度协方差矩阵的期望算子范数被仅依赖于$β$的常数所界定。这回答了Talagrand提出的开放问题,他证明了$C(β)(\log n)^8$的界。我们的结果通过建立协方差矩阵的近似公式得出,该公式由微分TAP方程并随后最优地控制相关误差项获得。我们通过展示在临界温度和低温下算子范数的发散下界来补充此结果。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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