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authorJaron Kent-Dobias <jaron@kent-dobias.com>2022-06-30 14:37:41 +0200
committerJaron Kent-Dobias <jaron@kent-dobias.com>2022-06-30 14:37:41 +0200
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Some figure work, and some interpretation work.
Diffstat (limited to 'frsb_kac-rice.tex')
-rw-r--r--frsb_kac-rice.tex49
1 files changed, 45 insertions, 4 deletions
diff --git a/frsb_kac-rice.tex b/frsb_kac-rice.tex
index 0896da4..180798a 100644
--- a/frsb_kac-rice.tex
+++ b/frsb_kac-rice.tex
@@ -497,10 +497,7 @@ E\rangle_2$.
\begin{figure}
- \centering
- \includegraphics{figs/316_complexity_contour_1.pdf}
- \hfill
- \includegraphics{figs/316_complexity_contour_2.pdf}
+ \includegraphics{figs/316_complexity.pdf}
\caption{
Complexity of dominant saddles (blue), marginal minima (yellow), and
@@ -514,6 +511,13 @@ E\rangle_2$.
\begin{figure}
\centering
+ \includegraphics{figs/316_complexity_contour_1.pdf}
+ \hfill
+ \includegraphics{figs/316_complexity_contour_2.pdf}
+\end{figure}
+
+\begin{figure}
+ \centering
\begin{minipage}{0.7\textwidth}
\includegraphics{figs/316_comparison_q.pdf}
\hspace{1em}
@@ -633,6 +637,43 @@ for different energies and typical vs minima.
\section{Interpretation}
+\begin{equation}
+ H(s)-h^Ts+g\xi^Ts
+\end{equation}
+Let $\langle A\mid\epsilon,\mu\rangle$ be average over stationary points with given $\epsilon$ and $\mu$, i.e.,
+\begin{equation}
+ \langle A\mid\epsilon,\mu\rangle
+ =\frac1{\mathcal N}
+ \int d\nu(s\mid\epsilon,\mu)\,A(s)
+\end{equation}
+with
+\begin{equation}
+ d\nu(s\mid\epsilon,\mu)=ds\,\delta(N\epsilon-H(s))\delta(\partial H(s)+\mu s)|\det(\partial\partial H(s)+\mu I)|
+\end{equation}
+\begin{equation}
+ \frac1N\|\langle s\mid\epsilon,\mu\rangle\|^2
+ =\lim_{n\to0}\int\prod_\alpha^nd\nu(s_\alpha\mid\epsilon,\mu)\,\left(\frac1{n(n-1)}\sum_{\alpha\neq\beta}\frac{s_\alpha^Ts_\beta}N\right)
+ =\lim_{n\to0}\frac1{n(n-1)}\left\langle\sum_{a\neq b}^nC_{ab}\right\rangle
+ =\int_0^1 dx\,c(x)
+\end{equation}
+
+\begin{equation}
+ \frac1N\sum_i\frac{\partial\langle s_i\rangle}{\partial h_i}
+ =\lim_{n\to0}\int\prod_\alpha^nd\nu(s_\alpha)\,\left(\frac1n\sum_{\alpha\beta}-i\frac{\hat s_\alpha^Ts_\beta}N\right)
+ =\lim_{n\to0}\frac1n\left\langle\sum_{\alpha\beta}R_{\alpha\beta}\right\rangle
+ =r_d-\int_0^1dx\,r(x)
+\end{equation}
+
+\begin{equation}
+ \begin{aligned}
+ \lim_{g\to0}\overline{\frac{\partial^2\Sigma}{\partial g^2}}
+ =\frac1N\lim_{g\to0}\lim_{n\to0}\frac1n\overline{\int\prod_\alpha d\nu(s_\alpha)\left(\sum_\alpha i\xi^T\hat s_\alpha\right)^2}
+ =\lim_{n\to0}\frac1n\int\prod_\alpha d\nu(s_\alpha)\left(\sum_{ab}-\frac{\hat s_a^T\hat s_b}N\right) \\
+ =-\lim_{n\to0}\frac1n\left\langle\sum_{ab}D_{ab}\right\rangle
+ =-d_d+\int_0^1dx\,d(x)
+ \end{aligned}
+\end{equation}
+
The
meaning of $R_{ab}$ is that of a response of replica $a$ to a linear field in
replica $b$: