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authorJaron Kent-Dobias <jaron@kent-dobias.com>2022-07-08 14:09:34 +0200
committerJaron Kent-Dobias <jaron@kent-dobias.com>2022-07-08 14:09:34 +0200
commit19b8c286ff58ac351117a2ad483c71bb8d398431 (patch)
tree184de23daf232642001e1b568061621367eaca33
parentf3de26844a01eb909501f3e73b60e2626f91b40c (diff)
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Merge branch 'master' of https://git.overleaf.com/629a30c097d0b9f4b4f7a69d
-rw-r--r--frsb_kac-rice.bib25
-rw-r--r--frsb_kac-rice.tex22
2 files changed, 38 insertions, 9 deletions
diff --git a/frsb_kac-rice.bib b/frsb_kac-rice.bib
index 2f7dd6d..d33028e 100644
--- a/frsb_kac-rice.bib
+++ b/frsb_kac-rice.bib
@@ -384,3 +384,28 @@
publisher={APS}
}
+
+@article{gamarnik2021overlap,
+ title={The overlap gap property and approximate message passing algorithms for $ p $-spin models},
+ author={Gamarnik, David and Jagannath, Aukosh},
+ journal={The Annals of Probability},
+ volume={49},
+ number={1},
+ pages={180--205},
+ year={2021},
+ publisher={Institute of Mathematical Statistics}
+}
+@article{huang2021tight,
+ title={Tight Lipschitz hardness for optimizing mean field spin glasses},
+ author={Huang, Brice and Sellke, Mark},
+ journal={arXiv preprint arXiv:2110.07847},
+ year={2021}
+}
+
+
+@article{alaoui2022sampling,
+ title={Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization},
+ author={Alaoui, Ahmed El and Montanari, Andrea and Sellke, Mark},
+ journal={arXiv preprint arXiv:2203.05093},
+ year={2022}
+}
diff --git a/frsb_kac-rice.tex b/frsb_kac-rice.tex
index 09c3042..1427085 100644
--- a/frsb_kac-rice.tex
+++ b/frsb_kac-rice.tex
@@ -52,11 +52,15 @@ replica-symmetry breaking scheme that is well-defined, and corresponds directly
to the topological characteristics of those minima.
-A more general question, of interest in optimization problems, is how to define a `threshold level'. This notion was introduced in Ref \cite{cugliandolo1993analytical} in the context of the $p$-spin model, as the energy at which the constant energy patches of phase-space percolate - hence
-explaining why dynamics should relax to that level.
+Perhaps the most interesting application of this computation is in the context of
+optimization problems, see for example \cite{gamarnik2021overlap,alaoui2022sampling,huang2021tight}. A question
+that appears there is how to define a `threshold level'. This notion was introduced \cite{cugliandolo1993analytical} in the context of the $p$-spin model, as the energy at which the patches of the same energy in phase-space percolate - hence
+explaining why dynamics never go below that level.
The notion of a `threshold' for more complex landscapes has later been
-attempted several times, never to our knowledge in a clear and unambiguous
-way. One of the purposes of this paper is to
+invoked several times, never to our knowledge in a clear and unambiguous
+way. One of the purposes of this paper is to give a sufficiently detailed
+characterization of a general landscape so that a meaningful general notion
+of threshold may be introduced - if this is at all possible.
\section{The model}
@@ -968,9 +972,9 @@ P(q)=\frac1{\mathcal N^2}\sum_{\mathbf s_1\in\mathcal S}\sum_{\mathbf s_2\in\mat
{\em This is the probability that two stationary points randomly drawn from the ensemble
of stationary points happen to be at overlap $q$}
-It is
-straightforward to show that moments of this distribution are related to
-certain averages of the form. These are evaluated for a given energy, index, etc, but
+%It is straightforward to show that moments of this distribution are related to
+%certain averages of the form.
+These are evaluated for a given energy, index, etc, but
we shall omit these subindices for simplicity.
\begin{equation}
@@ -1027,8 +1031,8 @@ Consider two independent pure $p$ spin models $H_{p_1}({\mathbf s})$ and $H_{p_2
{\mathbf \sigma} \cdot {\mathbf s}$.
The complexities are
\begin{eqnarray}
- e^{N\Sigma(e)}&=&\int de_1 de_2 \; e^{N[ \Sigma_1(e_1) + \Sigma_2(e_2) + O(\varepsilon) -\lambda N [(e_1+e_2)-e]}\nonumber \\
- e^{-G(\hat \beta)}&=&\int de de_1 de_2 \; e^{N[-\hat \beta e+ \Sigma_1(e_1) + \Sigma_2(e_2) + O(\varepsilon) -\lambda N [(e_1+e_2)-e]}
+ e^{N\Sigma(e)}&=&\int de_1 de_2 d\lambda \; e^{N[ \Sigma_1(e_1) + \Sigma_2(e_2) + O(\varepsilon) -\lambda N [(e_1+e_2)-e]}\nonumber \\
+ e^{-G(\hat \beta)}&=&\int de de_1 de_2 d\lambda\; e^{N[-\hat \beta e+ \Sigma_1(e_1) + \Sigma_2(e_2) + O(\varepsilon) -\lambda N [(e_1+e_2)-e]}
\end{eqnarray}
The maximum is given by $\Sigma_1'=\Sigma_2'=\hat \beta$, provided it occurs in the phase
in which both $\Sigma_1$ and $\Sigma_2$ are non-zero. The two systems are `thermalized',