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authorJaron Kent-Dobias <jaron@kent-dobias.com>2024-09-04 16:59:02 +0200
committerJaron Kent-Dobias <jaron@kent-dobias.com>2024-09-04 16:59:02 +0200
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Begin working on the complexity appendix.
-rw-r--r--topology.tex184
1 files changed, 166 insertions, 18 deletions
diff --git a/topology.tex b/topology.tex
index 9d0428a..4120cef 100644
--- a/topology.tex
+++ b/topology.tex
@@ -417,7 +417,7 @@ One way to definitely narrow possible interpretations of the average Euler
characteristic is to compute a complementary average. The Euler characteristic
is the alternating sum of numbers of critical points of different index. If
instead we make the direct sum
-\begin{equation}
+\begin{equation} \label{eq:abs.kac-rice}
\mathcal N_H(\Omega)=\sum_{i=0}^N\mathcal N_H(\text{index}=i)
=\int_\Omega d\mathbf x\,\delta\big(\nabla H(\mathbf x)\big)
\,\big|\det\operatorname{Hess}H(\mathbf x)\big|
@@ -443,15 +443,15 @@ To compute the complexity, we follow a similar procedure to the Euler
characteristic. The main difference lies in how we treat the absolute value
function around the determinant. Following \cite{Fyodorov_2004_Complexity}, we
make use of the identity
-\begin{equation}
+\begin{equation} \label{eq:fyodorov.nightmare}
\begin{aligned}
|\det A|
&=\lim_{\epsilon\to0}\frac{(\det A)^2}{\sqrt{\det(A+i\epsilon I)}\sqrt{\det(A-i\epsilon I)}}
\\
- &=\frac1{(2\pi)^N}\int d\bar{\pmb\eta}_1\,d\pmb\eta_1\,d\bar{\pmb\eta}_2\,d\pmb\eta_2\,d\mathbf a\,d\mathbf b\,
+ &=\frac1{(2\pi)^N}\int d\bar{\pmb\eta}_1\,d\pmb\eta_1\,d\bar{\pmb\eta}_2\,d\pmb\eta_2\,d\mathbf a_+\,d\mathbf a_-\,
e^{
-\bar{\pmb\eta}_1^TA\pmb\eta_1-\bar{\pmb\eta}_2^TA\pmb\eta_2
- -\frac12\mathbf a^T(A+i\epsilon I)\mathbf a-\frac12\mathbf b^T(A-i\epsilon I)\mathbf b
+ -\frac12\mathbf a_+^T(A+i\epsilon I)\mathbf a_+-\frac12\mathbf a_-^T(A-i\epsilon I)\mathbf a_-
}
\end{aligned}
\end{equation}
@@ -783,7 +783,7 @@ These new variables can replace $\pmb\phi$ in the integral using a generalized H
where we show the asymptotic value of the prefactor in Appendix~\ref{sec:prefactor}.
To move on from this expression,
we need to expand the superspace notation. We can write
-\begin{equation}
+\begin{equation} \label{eq:ops.q}
\begin{aligned}
\mathbb Q(1,2)
&=C-R(\bar\theta_1\theta_1+\bar\theta_2\theta_2)
@@ -800,7 +800,7 @@ and
\begin{equation}
\mathbb M(1)
=m+\bar\theta_1H_0+\bar H_0\theta_1+i\hat m\bar\theta_1\theta_1
- \label{eq:ops}
+ \label{eq:ops.m}
\end{equation}
with associated measures
\begin{align}
@@ -811,10 +811,10 @@ with associated measures
\label{eq:op.measures}
\end{align}
The order parameters $C$, $R$, $G$, $D$, $m$, and $\hat m$ are ordinary numbers defined by
-\begin{align}
+\begin{align} \label{eq:ops.bos}
C=\frac{\mathbf x\cdot\mathbf x}N
&&
- R=i\frac{\mathbf x\cdot\hat{\mathbf x}}N
+ R=-i\frac{\mathbf x\cdot\hat{\mathbf x}}N
&&
G=\frac{\bar{\pmb\eta}\cdot\pmb\eta}N
&&
@@ -822,17 +822,17 @@ The order parameters $C$, $R$, $G$, $D$, $m$, and $\hat m$ are ordinary numbers
&&
m=\frac{\mathbf x_0\cdot\mathbf x}N
&&
- \hat m=i\frac{\mathbf x_0\cdot\hat{\mathbf x}}N
+ \hat m=-i\frac{\mathbf x_0\cdot\hat{\mathbf x}}N
\end{align}
while $\bar H$, $H$, $\bar{\hat H}$, $\hat H$, $\bar H_0$ and $H_0$ are Grassmann numbers defined by
-\begin{align}
+\begin{align} \label{eq:ops.ferm}
\bar H=\frac{\bar{\pmb\eta}\cdot\mathbf x}N
&&
H=\frac{\pmb\eta\cdot\mathbf x}N
&&
- \bar{\hat H}=i\frac{\bar{\pmb\eta}\cdot\hat{\mathbf x}}N
+ \bar{\hat H}=-i\frac{\bar{\pmb\eta}\cdot\hat{\mathbf x}}N
&&
- \hat H=i\frac{\pmb\eta\cdot\hat{\mathbf x}}N
+ \hat H=-i\frac{\pmb\eta\cdot\hat{\mathbf x}}N
&&
\bar H_0=\frac{\bar{\pmb\eta}\cdot\mathbf x_0}N
&&
@@ -1072,35 +1072,183 @@ orders in $N$, since for odd-dimensional manifolds the Euler characteristic is
always zero. When $N+M+1$ is even, we have $\overline{\chi(\Omega)}=2$ to
leading order in $N$, as specified in the main text.
-\section{Details of the calculation of the average number of stationary points}
+\section{Details for the average number of stationary points}
\label{sec:complexity.details}
+Starting from \eqref{eq:abs.kac-rice}, we make the substitution
+\eqref{eq:delta.exp} to treat the Dirac $\delta$-function and use
+\eqref{eq:fyodorov.nightmare} to write the absolute value of the determinant as
+\begin{align}
+ &\big|\det\partial\partial L(\mathbf x,\pmb\omega)\big|
+ =\lim_{\epsilon\to0}\frac1{(2\pi)^{N+M+1}}\int d\bar{\pmb\eta}_1\,d\pmb\eta_1\,d\bar{\pmb\eta}_2\,d\pmb\eta_2\,
+ d\bar{\pmb\gamma}_1\,d\pmb\gamma_1\,d\bar{\pmb\gamma}_2\,d\pmb\gamma_2\,
+ d\mathbf a_+\,d\mathbf a_-\,d\mathbf b_+\,d\mathbf b_-\, \notag \\
+ &\qquad\times\exp\Bigg\{
+ -\frac12\begin{bmatrix}\mathbf a_+^T&\mathbf b_+^T\end{bmatrix}\big(\partial\partial L(\mathbf x,\pmb\omega)+i\epsilon I\big)\begin{bmatrix}\mathbf a_+\\\mathbf b_+\end{bmatrix}
+ -\frac12\begin{bmatrix}\mathbf a_-^T&\mathbf b_-^T\end{bmatrix}\big(\partial\partial L(\mathbf x,\pmb\omega)-i\epsilon I\big)\begin{bmatrix}\mathbf a_-\\\mathbf b_-\end{bmatrix}
+ \notag \\
+ &\hspace{6em}-\begin{bmatrix}\bar{\pmb\eta}_1^T&\bar{\pmb\gamma}_1^T\end{bmatrix}\partial\partial L(\mathbf x,\pmb\omega)\begin{bmatrix}\pmb\eta_1\\\pmb\gamma_1\end{bmatrix}
+ -\begin{bmatrix}\bar{\pmb\eta}_2^T&\bar{\pmb\gamma}_2^T\end{bmatrix}\partial\partial L(\mathbf x,\pmb\omega)\begin{bmatrix}\pmb\eta_2\\\pmb\gamma_2\end{bmatrix}
+ \Bigg\}
+ \label{eq:abs.det.exp}
+\end{align}
+where the $\mathbf a_\pm$ are in $\mathbb R^N$, the $\mathbf b_\pm$ are in
+$\mathbb R^M$, and the $\pmb\eta$ and $\pmb\gamma$ are Grassmann vectors just as in
+\eqref{eq:det.exp}, except with an extra copy of each. This zoo of vectors
+quickly becomes tiring. Thankfully, there is a way to compactly represent this
+calculation again using superspace vectors.
+
+Consider the vectors
\begin{align}
\pmb\phi(1,2)
&=\mathbf x
+\bar\theta_1\pmb\eta_1+\bar{\pmb\eta}_1\theta_1\bar\theta_2\theta_2
+\bar\theta_2\pmb\eta_2+\bar{\pmb\eta}_2\theta_2\bar\theta_1\theta_1 \\
- &\qquad+\frac1{\sqrt2}(\bar\theta_1\theta_2+\bar\theta_2\theta_1)\mathbf a
- +\frac i{\sqrt2}(\bar\theta_1\theta_1+\bar\theta_2\theta_2)\mathbf b
+ &\qquad+\frac1{\sqrt2}(\bar\theta_1\theta_2+\bar\theta_2\theta_1)\mathbf a_+
+ +\frac i{\sqrt2}(\bar\theta_1\theta_1+\bar\theta_2\theta_2)\mathbf a_-
+\bar\theta_1\theta_1\bar\theta_2\theta_2i\hat{\mathbf x}
\notag \\
\sigma_k(1,2)
&=\omega_k
+\bar\theta_1\gamma_{1k}+\bar{\gamma}_{1k}\theta_1\bar\theta_2\theta_2
+\bar\theta_2\gamma_{2k}+\bar{\gamma}_{2k}\theta_2\bar\theta_1\theta_1 \\
- &\qquad+\frac1{\sqrt2}(\bar\theta_1\theta_2+\bar\theta_2\theta_1)c_k
- +\frac i{\sqrt2}(\bar\theta_1\theta_1+\bar\theta_2\theta_2)d_k
+ &\qquad+\frac1{\sqrt2}(\bar\theta_1\theta_2+\bar\theta_2\theta_1)b_{-k}
+ +\frac i{\sqrt2}(\bar\theta_1\theta_1+\bar\theta_2\theta_2)b_{+k}
+\bar\theta_1\theta_1\bar\theta_2\theta_2\hat\omega_k
\notag
\end{align}
+The entire expression for the complexity of stationary points combining
+\eqref{eq:delta.exp} and \eqref{eq:abs.det.exp} can be expressed in the compact
+form
\begin{equation}
\mathcal N_H(\Omega)
=\lim_{\epsilon\to0}\int d\pmb\phi\,d\pmb\sigma\,e^{
\int d1\,d2\,L(\pmb\phi(1,2),\pmb\sigma(1,2))
-\frac{i\epsilon}2
- (\|\mathbf a\|^2-\|\mathbf b\|^2+\|\mathbf c\|^2-\|\mathbf d\|^2)
+ (\|\mathbf a_+\|^2-\|\mathbf a_-\|^2+\|\mathbf b_+\|^2-\|\mathbf b_-\|^2)
}
\end{equation}
+Note that unlike in the derivation of the average Euler characteristic,
+$\pmb\phi(1,2)$ does not span the entire superspace. Therefore, we will not be
+able to write expressions like the superdeterminant of
+$f(\pmb\phi^T\pmb\phi/N)$, which are formally zero.
+
+Following similar steps as for the Euler characteristic, we first take the average over the random constraint functions $V$. This yields
+\begin{equation}
+ \begin{aligned}
+ \overline{\mathcal N_H(\Omega)}
+ =\int d\pmb\phi\,d\pmb\sigma\,\exp\Bigg\{
+ \frac12\int d1\,d2\,d3\,d4\,\sum_{k=1}^M\sigma_k(1,2)\sigma_k(3,4)f\left(\frac{\pmb\phi(1,2)\cdot\pmb\phi(3,4)}N\right)
+ \\
+ +\int d1\,d2\left[
+ H(\pmb\phi(1,2))
+ +\frac12\sigma_0(1,2)\big(\|\pmb\phi(1,2)\|^2-N\big)
+ -V_0\sum_{k=1}^M\sigma_k(1,2)
+ \right]
+ \Bigg\}
+ \end{aligned}
+\end{equation}
+We once again have a Gaussian integral in the parameters inside the Lagrange
+multipliers $\sigma_k$ for $k=1,\ldots,M$. However, here we must expand the
+superfields at this stage. Before that, we introduce order parameters analogous
+to before. Alongside those in \eqref{eq:ops.bos} and \eqref{eq:ops.ferm}, we also define
+\begin{align}
+ A_{++}=\frac{\mathbf a_+\cdot\mathbf a_+}N
+ &&
+ A_{--}=\frac{\mathbf a_-\cdot\mathbf a_-}N
+ &&
+ A_{+-}=\frac{\mathbf a_+\cdot\mathbf a_-}N
+ &&
+ X_+=\frac{\mathbf x\cdot\mathbf a_+}N
+ &&
+ X_-=\frac{\mathbf x\cdot\mathbf a_-}N
+ \\
+ \hat X_+=-i\frac{\hat{\mathbf x}\cdot\mathbf a_+}N
+ &&
+ \hat X_-=-i\frac{\hat{\mathbf x}\cdot\mathbf a_-}N
+ &&
+ H_{ij}=\frac{\bar{\pmb\eta}_i\cdot\pmb\eta_j}N
+ &&
+ J=\frac{\pmb\eta_1\cdot\pmb\eta_2}N
+ &&
+ \bar J=\frac{\bar{\pmb\eta}_1\cdot\bar{\pmb\eta}_2}N
+\end{align}
+These come with a volume term in the action
+\begin{equation}
+ \begin{aligned}
+ \frac12\log\det\left(
+ \begin{bmatrix}
+ C & iR & X_+ & X_- \\
+ iR & D & i\hat X_+ & i\hat X_- \\
+ X_+ & i\hat X_+ & A_{++} & A_{+-} \\
+ X_- & i\hat X_- & A_{+-} & A_{--}
+ \end{bmatrix}
+ -\begin{bmatrix}
+ m \\
+ i\hat m \\
+ m_+ \\
+ m_-
+ \end{bmatrix}
+ \begin{bmatrix}
+ m &
+ i\hat m &
+ m_+ &
+ m_-
+ \end{bmatrix}
+ \right) \qquad \\
+ -\frac12\log\det\begin{bmatrix}
+ 0 & G_{11} & \bar J & G_{12} \\
+ -G_{11} & 0 & -G_{21} & J \\
+ -\bar J & G_{21} & 0 & G_{22} \\
+ -G_{12} & -J & -G_{22} & 0
+ \end{bmatrix}
+ \end{aligned}
+\end{equation}
+As before, we can immediately integrate out $\sigma_0$, which fixes certain of the order parameters. In particular, we find
+\begin{align}
+ C=1 &&
+ X_+=0 &&
+ X_-=0 &&
+ G_+=-R-\frac12(A_{++}+A_{--})
+\end{align}
+where we have defined the symmetric and antisymmetric combinations of $G_{11}$ and $G_{22}$
+\begin{align}
+ G_+=G_{11}+G_{22}
+ &&
+ G_-=G_{11}-G_{22}
+ &&
+ A_+=A_{11}+A_{22}
+ &&
+ A_-=A_{11}-A_{22}
+\end{align}
+Once the first three substitutions have been made, the result for the Gaussian
+integral in the ordinary variables $\omega_k$, $\hat\omega_k$, $b_{1k}$, and
+$b_{2k}$ is a kernel with
+\begin{equation}
+ K=\begin{bmatrix}
+ K_{11} & iRf'(1) & -\hat X_1 f'(1) & -\hat X_2 f'(1) \\
+ iRf'(1) & f(1) & 0 & 0 \\
+ -\hat X_1 f'(1) & 0 & i\epsilon-\tfrac12f'(1)(A_++A_-) & -A_{12}f'(1) \\
+ -\hat X_2 f'(1) & 0 & -A_{12}f'(1) & -i\epsilon-\tfrac12f'(1)(A_+-A_-)
+ \end{bmatrix}
+\end{equation}
+\begin{equation}
+ K_{11}=Df'(1)-\frac12f''(1)\left[
+ (R-\tfrac12A_+)^2+\tfrac12A_-^2+2A_{12}^2-(G_-^2+4G_{12}G_{21}+4\bar JJ)
+ \right]
+\end{equation}
+and likewise a Gaussian integral in the Grassmann variables with kernel
+\begin{equation}
+ K'=f'(1)\begin{bmatrix}
+ 0 & G_{11} & J & G_{21} \\
+ -G_{11} & 0 & -G_{12} & \bar J \\
+ -J & G_{12} & 0 & G_{22} \\
+ -G_{21} & -\bar J & -G_{22} & 0
+ \end{bmatrix}
+\end{equation}
+\
+
+
\section{The quenched shattering energy in {\oldstylenums 1}\textsc{frsb} models}
\label{sec:1frsb}