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author | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2024-09-19 08:50:01 +0200 |
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committer | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2024-09-19 08:50:01 +0200 |
commit | 43fc2fdf9e74351fdeb8abed136cbbd25ec69eb0 (patch) | |
tree | 25324f4c06fcf638a7395d559766277c032340f9 | |
parent | 7569d4b2ae6e519eab8d3398b804c32c9bb64ad8 (diff) | |
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Reorganization of more things to the appendix.
-rw-r--r-- | topology.tex | 169 |
1 files changed, 82 insertions, 87 deletions
diff --git a/topology.tex b/topology.tex index 127b15e..f56a2c7 100644 --- a/topology.tex +++ b/topology.tex @@ -271,71 +271,31 @@ The integral over the solution manifold $\Omega$ in \eqref{eq:kac-rice} becomes \end{equation} where $\partial=[\frac\partial{\partial\mathbf x},\frac\partial{\partial\pmb\omega}]$ is the vector of partial derivatives with respect to all $N+M+1$ variables. -This integral is now in a form where standard techniques from the mean-field -theory of disordered systems can be applied to calculate it. +This expression is now in a form where standard techniques from the mean-field +theory of disordered systems can be applied to average over the random constraint functions and evaluate the integrals to leading order in large $N$. -To evaluate the average of $\chi$ over the random constraints, we first translate the $\delta$-functions and determinant to integral form, with -\begin{align} - \label{eq:delta.exp} - \delta\big(\partial L(\mathbf x,\pmb\omega)\big) - &=\int\frac{d\hat{\mathbf x}}{(2\pi)^N}\frac{d\hat{\pmb\omega}}{(2\pi)^{M+1}} - \,e^{i[\hat{\mathbf x},\hat{\pmb\omega}]\cdot\partial L(\mathbf x,\pmb\omega)} - \\ - \label{eq:det.exp} - \det\partial\partial L(\mathbf x,\pmb\omega) - &=\int d\bar{\pmb\eta}\,d\pmb\eta\,d\bar{\pmb\gamma}\,d\pmb\gamma\, - e^{-[\bar{\pmb\eta},\bar{\pmb\gamma}]^T\partial\partial L(\mathbf x,\pmb\omega)[\pmb\eta,\pmb\gamma]} -\end{align} -where $\hat{\mathbf x}$ and $\hat{\pmb\omega}$ are ordinary vectors and -$\bar{\pmb\eta}$, $\pmb\eta$, $\bar{\pmb\gamma}$, and $\pmb\gamma$ are -Grassmann vectors. With these expressions substituted into -\eqref{eq:kac-rice.lagrange}, the result is an integral over an exponential -whose argument is linear in the random functions $V_k$. These functions can -therefore be averaged over, and the resulting expression treated with standard -methods. Details of this calculation can be found in Appendix~\ref{sec:euler}. -The result is the reduction of the average Euler characteristic to an expression of the -form -\begin{equation} \label{eq:pre-saddle.characteristic} - \overline{\chi(\Omega)} - =\left(\frac N{2\pi}\right)^2\int dR\,dD\,dm\,d\hat m\,g(R,D,m,\hat m)\,e^{N\mathcal S_\chi(R,D,m,\hat m)} +Details of this calculation can be found in Appendix~\ref{sec:euler}. The +result is the reduction of the average Euler characteristic to an integral over +a single order parameter $m=\frac1N\mathbf x\cdot\mathbf x_0$ of the form +\begin{equation} + \overline{\chi(\Omega)}=\left(\frac{N}{2\pi}\right)^{\frac12}\int dm\,g(m)\,e^{N\mathcal S_\chi(m)} \end{equation} -where $g$ is a prefactor of $o(N^0)$, $\mathcal S_\chi$ is an effective action defined by -\begin{equation} \label{eq:euler.action} - \begin{aligned} - \mathcal S_\chi(R,D,m,\hat m) - &=-\hat m-\frac\alpha2\left[ - \log\left(1+\frac{f(1)D}{f'(1)R^2}\right) - +\frac{V_0^2}{f(1)}\left(1+\frac{f'(1)R^2}{f(1)D}\right)^{-1} - \right] \\ - &\hspace{7em}+\frac12\log\left( - 1+\frac{(1-m^2)D+\hat m^2-2Rm\hat m}{R^2} +where $g(m)$ is a prefactor of order $N^0$ and $\mathcal S_\chi(m)$ is an +effective action defined by +\begin{equation} \label{eq:S.m} + \mathcal S_\chi(m) + =-\frac\alpha2\bigg[ + \log\left( + 1-\frac{f(1)}{f'(1)}\frac{1+\frac m{R_*}}{1-m^2} \right) - \end{aligned} + +\frac{V_0^2}{f(1)}\left( + 1-\frac{f'(1)}{f(1)}\frac{1-m^2}{1+\frac m{R_*}} + \right)^{-1} + \bigg] + +\frac12\log\left(-\frac m{R_*}\right) \end{equation} -and where we have introduced the ratio $\alpha=M/N$. -The remaining order parameters are defined by the scalar products -\begin{align} - R=-i\frac1N\mathbf x\cdot\hat{\mathbf x} - && - D=\frac1N\hat{\mathbf x}\cdot\hat{\mathbf x} - && - m=\frac1N\mathbf x\cdot\mathbf x_0 - && - \hat m=-i\frac1N\hat {\mathbf x}\cdot\mathbf x_0 -\end{align} -between the configurations $\mathbf x$, the auxiliary configurations -$\hat{\mathbf x}$, and the height axis $\mathbf x_0$. -The integral \eqref{eq:pre-saddle.characteristic} can be evaluated to leading -order in $N$ by a saddle point approximation. First we extremize with respect -to $R$, $D$, and $\hat m$, which take the saddle-point values -\begin{align} - R=R^* - && - D=-\frac{m+R_*}{1-m^2}R_* - && - \hat m=0 -\end{align} -where we have defined +Here we have introduced the ratio $\alpha=M/N$ between the number of equations +and the number of variables, and $R_*$ is a function of $m$ given by \begin{equation} \label{eq:rs} \begin{aligned} R_* @@ -350,28 +310,15 @@ where we have defined \Bigg] \end{aligned} \end{equation} -Upon substitution of these solutions into \eqref{eq:euler.action}, we find an -effective action as a function of $m$ alone given by -\begin{equation} \label{eq:S.m} - \mathcal S_\chi(m) - =-\frac\alpha2\bigg[ - \log\left( - 1-\frac{f(1)}{f'(1)}\frac{1+\frac m{R_*}}{1-m^2} - \right) - +\frac{V_0^2}{f(1)}\left( - 1-\frac{f'(1)}{f(1)}\frac{1-m^2}{1+\frac m{R_*}} - \right)^{-1} - \bigg] - +\frac12\log\left(-\frac m{R_*}\right) -\end{equation} -This function is plotted in Fig.~\ref{fig:action} for a -selection of parameters. -To finish evaluating the integral, this -expression should be maximized with respect to $m$. If $m_*$ is such a maximum, -then the resulting Euler characteristic is $\overline{\chi(\Omega)}\propto -e^{N\mathcal S_\chi(m_*)}$. - -\begin{figure}[tbh] +This function is plotted in Fig.~\ref{fig:action} for a selection of +parameters. To finish evaluating the integral by the saddle-point +approximation, the action should be maximized with respect to $m$. If $m_*$ is +such a maximum, then the resulting average Euler characteristic is +$\overline{\chi(\Omega)}\propto e^{N\mathcal S_\chi(m_*)}$. In the next +subsection we examine the maxima of $\mathcal S_\chi$ their properties as the +parameters are varied. + +\begin{figure}[tb] \includegraphics{figs/action_1.pdf} \hspace{-3.5em} \includegraphics{figs/action_3.pdf} @@ -834,9 +781,27 @@ JK-D is supported by a \textsc{DynSysMath} Specific Initiative of the INFN. \section{Details of the calculation of the average Euler characteristic} \label{sec:euler} -Our starting point is the expression \eqref{eq:kac-rice.lagrange} with the -substitutions of the $\delta$-function and determinant \eqref{eq:delta.exp} and -\eqref{eq:det.exp} made. To make the calculation compact, we introduce +Our starting point is the expression \eqref{eq:kac-rice.lagrange}. To evaluate +the average of $\chi$ over the random constraints, we first translate the +$\delta$-function and determinant to integral form, with +\begin{align} + \label{eq:delta.exp} + \delta\big(\partial L(\mathbf x,\pmb\omega)\big) + &=\int\frac{d\hat{\mathbf x}}{(2\pi)^N}\frac{d\hat{\pmb\omega}}{(2\pi)^{M+1}} + \,e^{i[\hat{\mathbf x},\hat{\pmb\omega}]\cdot\partial L(\mathbf x,\pmb\omega)} + \\ + \label{eq:det.exp} + \det\partial\partial L(\mathbf x,\pmb\omega) + &=\int d\bar{\pmb\eta}\,d\pmb\eta\,d\bar{\pmb\gamma}\,d\pmb\gamma\, + e^{-[\bar{\pmb\eta},\bar{\pmb\gamma}]^T\partial\partial L(\mathbf x,\pmb\omega)[\pmb\eta,\pmb\gamma]} +\end{align} +where $\hat{\mathbf x}$ and $\hat{\pmb\omega}$ are ordinary vectors and +$\bar{\pmb\eta}$, $\pmb\eta$, $\bar{\pmb\gamma}$, and $\pmb\gamma$ are +Grassmann vectors. With these expressions substituted into +\eqref{eq:kac-rice.lagrange}, the result is an integral over an exponential +whose argument is linear in the random functions $V_k$. + +To make the calculation compact, we introduce superspace coordinates \cite{DeWitt_1992_Supermanifolds, Kent-Dobias_2024_Conditioning}. Introducing the Grassmann indices $\bar\theta_1$ and $\theta_1$, we define the supervectors \begin{align} @@ -1007,7 +972,37 @@ as removing all dependence on $\bar H$ and $H$. With these solutions inserted, t \end{align} The Grassmann terms in these expressions do not contribute to the effective action, but will be important in our derivation of the prefactor for the -exponential around the stationary points at $\pm m_*$. The substitution of these expressions into \eqref{eq:post.hubbard-strat} without the Grassmann terms yields \eqref{eq:euler.action} from the main text. +exponential around the stationary points at $\pm m_*$. The substitution of these expressions into \eqref{eq:post.hubbard-strat} without the Grassmann terms yields +\begin{equation} \label{eq:pre-saddle.characteristic} + \overline{\chi(\Omega)} + =\left(\frac N{2\pi}\right)^2\int dR\,dD\,dm\,d\hat m\,g(R,D,m,\hat m)\,e^{N\mathcal S_\chi(R,D,m,\hat m)} +\end{equation} +where $g$ is a prefactor of $o(N^0)$ detailed in the following appendix, $\mathcal S_\chi$ is an effective action defined by +\begin{equation} \label{eq:euler.action} + \begin{aligned} + \mathcal S_\chi(R,D,m,\hat m) + &=-\hat m-\frac\alpha2\left[ + \log\left(1+\frac{f(1)D}{f'(1)R^2}\right) + +\frac{V_0^2}{f(1)}\left(1+\frac{f'(1)R^2}{f(1)D}\right)^{-1} + \right] \\ + &\hspace{7em}+\frac12\log\left( + 1+\frac{(1-m^2)D+\hat m^2-2Rm\hat m}{R^2} + \right) + \end{aligned} +\end{equation} +and where we have introduced the ratio $\alpha=M/N$. +The integral \eqref{eq:pre-saddle.characteristic} can be evaluated to leading +order in $N$ by a saddle point approximation. To get the formula \eqref{eq:S.m} +in the main text, we first extremize this expression with respect to $R$, $D$, +and $\hat m$, which take the saddle-point values +\begin{align} + R=R^* + && + D=-\frac{m+R_*}{1-m^2}R_* + && + \hat m=0 +\end{align} +where $R^*$ is given by \eqref{eq:rs} from the main text. \section{Calculation of the prefactor of the average Euler characteristic} \label{sec:prefactor} |