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author | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2024-09-04 16:59:02 +0200 |
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committer | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2024-09-04 16:59:02 +0200 |
commit | 4e0fc450da1c3b293f232e218cd6d87717e7adb5 (patch) | |
tree | 0d2252a99c5dfc9315e85615a7331bea1bb38c41 | |
parent | 2f17fb311918383785ab053eb73e83be803ec8ae (diff) | |
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Begin working on the complexity appendix.
-rw-r--r-- | topology.tex | 184 |
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} |