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-rw-r--r--bezout.tex449
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diff --git a/appeal.tex b/appeal.tex
new file mode 100644
index 0000000..6af3109
--- /dev/null
+++ b/appeal.tex
@@ -0,0 +1,77 @@
+\documentclass[a4paper]{letter}
+
+\usepackage[utf8]{inputenc} % why not type "Bézout" with unicode?
+\usepackage[T1]{fontenc} % vector fonts plz
+\usepackage{newtxtext,newtxmath} % Times for PR
+\usepackage[
+ colorlinks=true,
+ urlcolor=purple,
+ linkcolor=black,
+ citecolor=black,
+ filecolor=black
+]{hyperref} % ref and cite links with pretty colors
+\usepackage{xcolor}
+\usepackage[style=phys]{biblatex}
+
+\addbibresource{bezout.bib}
+
+\signature{
+ \vspace{-6\medskipamount}
+ \smallskip
+ Jaron Kent-Dobias \& Jorge Kurchan
+}
+
+\address{
+ Laboratoire de Physique\\
+ Ecole Normale Sup\'erieure\\
+ 24 rue Lhomond\\
+ 75005 Paris
+}
+
+\begin{document}
+\begin{letter}{
+ Editorial Office\\
+ Physical Review Letters\\
+ 1 Research Road\\
+ Ridge, NY 11961
+}
+
+\opening{To the editors of Physical Review,}
+
+We wish to appeal your decision on our manuscript \emph{Complex complex
+landscapes}, which received a single referee report.
+
+We believe that the referee's overall criticisms of our paper are not entirely
+justified (and above all, difficult to answer). We have, however, submitted a
+revised manuscript clarifying the specific aspects that the referee found trying.
+
+The referee seems particularly worried that we have cited articles that are not
+themselves sufficiently cited, so we thought it may be useful propose a set of
+referees that are beyond suspicion of incompetence or uncitedness:
+
+\begin{tabular}{ll}
+ G Ben Arous & Courant \\
+ M Berry & Bristol\\
+ Y Fyodorov & King's College London \\
+ Daniel Fisher& Stanford\\
+ T Lubensky & U Penn\\
+ M Moore & Manchester \\
+ E Witten & IAS Princeton
+\end{tabular}
+
+We have also pointed out some very first results of the geometric implications for a
+random complex landscape of our calculation, something we plan to expend on in
+a future full article.
+
+Let us conclude by remarking that, although it is probably true that this paper will not
+make more than one hundred citations next year, we are confident that it will still
+be considered relevant in ten years time.
+
+
+\closing{Sincerely,}
+
+\vspace{1em}
+
+\end{letter}
+
+\end{document}
diff --git a/bezout.tex b/bezout.tex
index d7f4dc7..ed03a28 100644
--- a/bezout.tex
+++ b/bezout.tex
@@ -42,69 +42,100 @@
\maketitle
-Spin-glasses have long been considered the paradigm of many variable `complex
-landscapes,' a subject that includes neural networks and optimization problems,
-most notably constraint satisfaction \cite{Mezard_2009_Information}. The most tractable family of these
-are the mean-field spherical $p$-spin models \cite{Crisanti_1992_The} (for a
-review see \cite{Castellani_2005_Spin-glass}) defined by the energy
+Spin-glasses are the paradigm of many-variable `complex landscapes,' a category
+that also includes neural networks and optimization problems like constraint
+satisfaction \cite{Mezard_2009_Information}. The most tractable family of
+these are the mean-field spherical $p$-spin models \cite{Crisanti_1992_The}
+(for a review see \cite{Castellani_2005_Spin-glass}) defined by the energy
\begin{equation} \label{eq:bare.hamiltonian}
H_0 = \frac1{p!}\sum_{i_1\cdots i_p}^NJ_{i_1\cdots i_p}z_{i_1}\cdots z_{i_p},
\end{equation}
where $J$ is a symmetric tensor whose elements are real Gaussian variables and
-$z\in\mathbb R^N$ is constrained to the sphere $z^2=N$. This problem has been
+$z\in\mathbb R^N$ is constrained to the sphere $z^Tz=N$. This problem has been
studied in the algebra \cite{Cartwright_2013_The} and probability literature
\cite{Auffinger_2012_Random, Auffinger_2013_Complexity}. It has been attacked
from several angles: the replica trick to compute the Boltzmann--Gibbs
distribution \cite{Crisanti_1992_The}, a Kac--Rice \cite{Kac_1943_On,
Rice_1939_The, Fyodorov_2004_Complexity} procedure (similar to the
Fadeev--Popov integral) to compute the number of saddle-points of the energy
-function \cite{Crisanti_1995_Thouless-Anderson-Palmer}, and the
-gradient-descent---or more generally Langevin---dynamics staring from a
-high-energy configuration \cite{Cugliandolo_1993_Analytical}. Thanks to the
-simplicity of the energy, all these approaches yield analytic results in the
-large-$N$ limit.
+function \cite{Crisanti_1995_Thouless-Anderson-Palmer}, and gradient-descent
+(or more generally Langevin) dynamics starting from a high-energy configuration
+\cite{Cugliandolo_1993_Analytical}. Thanks to the simplicity of the energy, all
+these approaches yield analytic results in the large-$N$ limit.
In this paper we extend the study to complex variables: we shall take
$z\in\mathbb C^N$ and $J$ to be a symmetric tensor whose elements are
\emph{complex} normal, with $\overline{|J|^2}=p!/2N^{p-1}$ and
$\overline{J^2}=\kappa\overline{|J|^2}$ for complex parameter $|\kappa|<1$. The
-constraint remains $z^2=N$.
+constraint remains $z^Tz=N$.
-The motivations for this paper are of two types. On the practical side, there
+The motivations for this paper are of three types. On the practical side, there
are indeed situations in which complex variables appear naturally in disordered
problems: such is the case in which the variables are \emph{phases}, as in
random laser problems \cite{Antenucci_2015_Complex}. Quiver Hamiltonians---used
to model black hole horizons in the zero-temperature limit---also have a
Hamiltonian very close to ours \cite{Anninos_2016_Disordered}. A second reason
is that, as we know from experience, extending a real problem to the complex
-plane often uncovers underlying simplicity that is otherwise hidden, sheding
+plane often uncovers underlying simplicity that is otherwise hidden, shedding
light on the original real problem, e.g., as in the radius of convergence of a
series.
-Deforming an integral in $N$ real variables to a surface of dimension $N$ in
-$2N$-dimensional complex space has turned out to be necessary for correctly
-defining and analyzing path integrals with complex action (see
+Finally, deforming an integral in $N$ real variables to a surface of dimension
+$N$ in $2N$-dimensional complex space has turned out to be necessary for
+correctly defining and analyzing path integrals with complex action (see
\cite{Witten_2010_A, Witten_2011_Analytic}), and as a useful palliative for the
sign problem \cite{Cristoforetti_2012_New, Tanizaki_2017_Gradient,
-Scorzato_2016_The}. In order to do this correctly, the features of landscape
-of the action in complex space---like the relative position of its
-saddles---must be understood. Such landscapes are in general not random: here
-we propose to follow the strategy of computer science of understanding the
-generic features of random instances, expecting that this sheds light on the
-practical, nonrandom problems.
+Scorzato_2016_The}. In order to do this correctly, features of the action's
+landscape in complex space---such as the relative position of saddles and the
+existence of Stokes lines joining them---must be understood. This is typically
+done for simple actions with few saddles, or for a target phenomenology with
+symmetries that restrict the set of saddles to few candidates. Given the recent
+proliferation of `glassiness' in condensed matter and high energy physics, it
+is inevitable that someone will want to apply these methods to a system with a
+complex landscape, and will find they cannot use approaches that rely on such
+assumptions. Their landscape may not be random: here we follow the standard
+strategy of computer science by understanding the generic features of random
+instances of a simple case, expecting that this sheds light on practical,
+nonrandom problems. While in this paper we do not yet address analytic
+continuation of integrals, understanding the distribution and spectra of
+critical points is an essential first step.
Returning to our problem, the spherical constraint is enforced using the method
-of Lagrange multipliers: introducing $\epsilon\in\mathbb C$, our energy is
+of Lagrange multipliers: introducing $\epsilon\in\mathbb C$, our constrained
+energy is
\begin{equation} \label{eq:constrained.hamiltonian}
H = H_0+\frac p2\epsilon\left(N-\sum_i^Nz_i^2\right).
\end{equation}
-We choose to constrain our model by $z^2=N$ rather than $|z|^2=N$ in order to
-preserve the analyticity of $H$. The nonholomorphic constraint also has a
-disturbing lack of critical points nearly everywhere: if $H$ were so
-constrained, then $0=\partial^* H=-p\epsilon z$ would only be satisfied for
-$\epsilon=0$.
-
-The critical points are of $H$ given by the solutions to the set of equations
+One might balk at the constraint $z^Tz=N$---which could appropriately be called
+a \emph{hyperbolic} constraint---by comparison with $z^\dagger z=N$. The reasoning
+behind the choice is twofold.
+
+First, we seek to draw conclusions from our model that are applicable to generic
+holomorphic functions without any symmetry. Samples of $H_0$ nearly provide
+this, save for a single anomaly: the value of the energy and its gradient at
+any point $z$ correlate along the $z$ direction, with $\overline{H_0\partial
+H_0}\propto \overline{H_0(\partial H_0)^*}\propto z$. This anomalous direction
+should thus be forbidden, and the constraint surface $z^Tz=N$ accomplishes this.
+
+Second, taking the constraint to be the level set of a holomorphic function
+means the resulting configuration space is a \emph{bone fide} complex manifold,
+and therefore permits easy generalization of the integration techniques
+referenced above. The same cannot be said for the space defined by $z^\dagger
+z=N$, which is topologically the $(2N-1)$-sphere and cannot admit a complex
+structure.
+
+Imposing the constraint with a holomorphic function
+makes the resulting configuration space a \emph{bone fide} complex manifold, which is, as we mentioned, the
+situation we wish to model. The same cannot be said for the space defined by $z^\dagger
+z=N$, which is topologically the $(2N-1)$-sphere, does not admit a complex
+structure, and thus yields a trivial structure of saddles.
+However, we will introduce the bound $r^2\equiv z^\dagger z/N\leq R^2$
+on the `radius' per spin as a device to classify saddles. We shall see that this
+`radius' $r$ and its upper bound $R$ are insightful knobs in our present
+problem, revealing structure as they are varied. Note that taking $R=1$ reduces
+the problem to that of the ordinary $p$-spin.
+
+The critical points are of $H$ given by the solutions to
\begin{equation} \label{eq:polynomial}
\frac{p}{p!}\sum_{j_1\cdots j_{p-1}}^NJ_{ij_1\cdots j_{p-1}}z_{j_1}\cdots z_{j_{p-1}}
= p\epsilon z_i
@@ -114,20 +145,16 @@ equations of degree $p-1$, to which one must add the constraint. In this sense
this study also provides a complement to the work on the distribution of zeroes
of random polynomials \cite{Bogomolny_1992_Distribution}, which are for $N=1$
and $p\to\infty$. We see from \eqref{eq:polynomial} that at any critical
-point, $\epsilon=H/N$, the average energy.
-
-Since $H$ is holomorphic, any critical point of $\operatorname{Re}H$ is also a
-critical point of $\operatorname{Im}H$. The number of critical points of $H$ is
-therefore the same as that of $\operatorname{Re}H$. From each saddle
-emerge gradient lines of $\operatorname{Re}H$, which are also ones of constant
-$\operatorname{Im}H$ and therefore constant phase.
+point $\epsilon=H_0/N$, the average energy.
-Writing $z=x+iy$, $\operatorname{Re}H$ can be considered a real-valued function
-of $2N$ real variables. Its number of saddle-points is given by the usual
-Kac--Rice formula:
+Since $H$ is holomorphic, any critical point of $\operatorname{Re}H$ is also
+one of $\operatorname{Im}H$, and therefore of $H$ itself. Writing $z=x+iy$ for
+$x,y\in\mathbb R^N$, $\operatorname{Re}H$ can be considered a real-valued
+function of $2N$ real variables. The number of critical points of $H$ is thus given by the
+usual Kac--Rice formula applied to $\operatorname{Re}H$:
\begin{equation} \label{eq:real.kac-rice}
\begin{aligned}
- \mathcal N_J&(\kappa,\epsilon)
+ \mathcal N&(\kappa,\epsilon,R)
= \int dx\,dy\,\delta(\partial_x\operatorname{Re}H)\delta(\partial_y\operatorname{Re}H) \\
&\hspace{6pc}\times\left|\det\begin{bmatrix}
\partial_x\partial_x\operatorname{Re}H & \partial_x\partial_y\operatorname{Re}H \\
@@ -135,14 +162,21 @@ Kac--Rice formula:
\end{bmatrix}\right|.
\end{aligned}
\end{equation}
-The Cauchy--Riemann equations may be used to write this in a manifestly complex
-way. With the Wirtinger derivative $\partial=\frac12(\partial_x-i\partial_y)$,
-one can write $\partial_x\operatorname{Re}H=\operatorname{Re}\partial H$ and
+This expression is to be averaged over $J$ to give the complexity $\Sigma$ as
+$N \Sigma= \overline{\log\mathcal N}$, a calculation that involves the replica
+trick. Based on the experience from similar problems \cite{Castellani_2005_Spin-glass}, the
+\emph{annealed approximation} $N \Sigma \sim \log \overline{ \mathcal N}$ is
+expected to be exact wherever the complexity is positive.
+
+The Cauchy--Riemann equations may be used to write \eqref{eq:real.kac-rice} in
+a manifestly complex way. With the Wirtinger derivative
+$\partial\equiv\frac12(\partial_x-i\partial_y)$, one can write
+$\partial_x\operatorname{Re}H=\operatorname{Re}\partial H$ and
$\partial_y\operatorname{Re}H=-\operatorname{Im}\partial H$. Carrying these
-transformations through, we have
+transformations through, one finds
\begin{equation} \label{eq:complex.kac-rice}
\begin{aligned}
- \mathcal N_J&(\kappa,\epsilon)
+ \mathcal N&(\kappa,\epsilon,r)
= \int dx\,dy\,\delta(\operatorname{Re}\partial H)\delta(\operatorname{Im}\partial H) \\
&\hspace{6pc}\times\left|\det\begin{bmatrix}
\operatorname{Re}\partial\partial H & -\operatorname{Im}\partial\partial H \\
@@ -155,8 +189,9 @@ transformations through, we have
\end{aligned}
\end{equation}
This gives three equivalent expressions for the determinant of the Hessian: as
-that of a $2N\times 2N$ real matrix, that of an $N\times N$ Hermitian matrix,
-i.e. the norm squared of that of an $N\times N$ complex symmetric matrix.
+that of a $2N\times 2N$ real symmetric matrix, that of the $N\times N$ Hermitian
+matrix $(\partial\partial H)^\dagger\partial\partial H$, or the norm squared of
+that of the $N\times N$ complex symmetric matrix $\partial\partial H$.
These equivalences belie a deeper connection between the spectra of the
corresponding matrices. Each positive eigenvalue of the real matrix has a
@@ -167,57 +202,13 @@ Hessian is therefore the same as the distribution of singular values of
$\partial\partial H$, or the distribution of square-rooted eigenvalues of
$(\partial\partial H)^\dagger\partial\partial H$.
-The expression \eqref{eq:complex.kac-rice} is to be averaged over $J$ to give
-the complexity $\Sigma$ as $N \Sigma= \overline{\log\mathcal N} = \int dJ \,
-\log \mathcal N_J$, a calculation that involves the replica trick. In most the
-parameter-space that we shall study here, the \emph{annealed approximation} $N
-\Sigma \sim \log \overline{ \mathcal N} = \log\int dJ \, \mathcal N_J$ is
-exact.
-
A useful property of the Gaussian $J$ is that gradient and Hessian at fixed
-$\epsilon$ are statistically independent \cite{Bray_2007_Statistics,
+energy $\epsilon$ are statistically independent \cite{Bray_2007_Statistics,
Fyodorov_2004_Complexity}, so that the $\delta$-functions and the Hessian may
-be averaged independently. The $\delta$-functions are converted to exponentials
-by the introduction of auxiliary fields $\hat z=\hat x+i\hat y$. The average
-of those factors over $J$ can then be performed. A generalized
-Hubbard--Stratonovich allows a change of variables from the $4N$ original
-and auxiliary fields to eight bilinears defined by $Na=|z|^2$, $N\hat a=|\hat
-z|^2$, $N\hat c=\hat z^2$, $Nb=\hat z^*z$, and $Nd=\hat zz$ (and their
-conjugates). The result, to leading order in $N$, is
-\begin{equation} \label{eq:saddle}
- \overline{\mathcal N}(\kappa,\epsilon)
- = \int da\,d\hat a\,db\,db^*d\hat c\,d\hat c^*dd\,dd^*e^{Nf(a,\hat a,b,\hat c,d)},
-\end{equation}
-where the argument of the exponential is
-\begin{widetext}
- \begin{equation}
- f=2+\frac12\log\det\frac12\begin{bmatrix}
- 1 & a & d & b \\
- a & 1 & b^* & d^* \\
- d & b^* & \hat c & \hat a \\
- b & d^* & \hat a & \hat c^*
- \end{bmatrix}
- +\int d\lambda\,\rho(\lambda)\log|\lambda|^2
- +p\operatorname{Re}\left\{
- \frac18\left[\hat aa^{p-1}+(p-1)|d|^2a^{p-2}+\kappa(\hat c^*+(p-1)b^2)\right]-\epsilon b
- \right\}.
- \end{equation}
- The integral of the distribution $\rho$ of eigenvalues of $\partial\partial
- H$ comes from the Hessian and is dependant on $a$ alone. This function has an
- extremum in $\hat a$, $b$, $\hat c$, and $d$ at which its value is
- \begin{equation} \label{eq:free.energy.a}
- f(a)=1+\frac12\log\left(\frac4{p^2}\frac{a^2-1}{a^{2(p-1)}-|\kappa|^2}\right)+\int d\lambda\,\rho(\lambda)\log|\lambda|^2
- -2C_+[\operatorname{Re}(\epsilon e^{-i\theta})]^2-2C_-[\operatorname{Im}(\epsilon e^{-i\theta})]^2,
- \end{equation}
-\end{widetext}
-where $\theta=\frac12\arg\kappa$ and
-\begin{equation}
- C_{\pm}=\frac{a^p(1+p(a^2-1))\mp a^2|\kappa|}{a^{2p}\pm(p-1)a^p(a^2-1)|\kappa|-a^2|\kappa|^2}.
-\end{equation}
-This leaves a single parameter, $a$, which dictates the magnitude of $|z|^2$,
-or alternatively the magnitude $y^2$ of the imaginary part. The latter vanishes
-as $a\to1$, where (as we shall see) one recovers known results for the real
-$p$-spin.
+be averaged independently. First we shall compute the spectrum of the Hessian,
+which can in turn be used to compute the determinant. Then we will treat the
+$\delta$-functions and the resulting saddle point equations. The results of
+these calculations begin around \eqref{eq:bezout}.
The Hessian $\partial\partial H=\partial\partial H_0-p\epsilon I$ is equal to
the unconstrained Hessian with a constant added to its diagonal. The eigenvalue
@@ -229,28 +220,28 @@ Hessian of the unconstrained Hamiltonian is
=\frac{p(p-1)}{p!}\sum_{k_1\cdots k_{p-2}}^NJ_{ijk_1\cdots k_{p-2}}z_{k_1}\cdots z_{k_{p-2}},
\end{equation}
which makes its ensemble that of Gaussian complex symmetric matrices, when the
-direction along the constraint is neglected. Given its variances
-$\overline{|\partial_i\partial_j H_0|^2}=p(p-1)a^{p-2}/2N$ and
+anomalous direction normal to the constraint surface is neglected. Given its variances
+$\overline{|\partial_i\partial_j H_0|^2}=p(p-1)r^{p-2}/2N$ and
$\overline{(\partial_i\partial_j H_0)^2}=p(p-1)\kappa/2N$, $\rho_0(\lambda)$ is
constant inside the ellipse
\begin{equation} \label{eq:ellipse}
- \left(\frac{\operatorname{Re}(\lambda e^{i\theta})}{a^{p-2}+|\kappa|}\right)^2+
- \left(\frac{\operatorname{Im}(\lambda e^{i\theta})}{a^{p-2}-|\kappa|}\right)^2
- <\frac{p(p-1)}{2a^{p-2}}
+ \left(\frac{\operatorname{Re}(\lambda e^{i\theta})}{r^{p-2}+|\kappa|}\right)^2+
+ \left(\frac{\operatorname{Im}(\lambda e^{i\theta})}{r^{p-2}-|\kappa|}\right)^2
+ <\frac{p(p-1)}{2r^{p-2}}
\end{equation}
where $\theta=\frac12\arg\kappa$ \cite{Nguyen_2014_The}. The eigenvalue
spectrum of $\partial\partial H$ is therefore constant inside the same ellipse
translated so that its center lies at $-p\epsilon$. Examples of these
distributions are shown in the insets of Fig.~\ref{fig:spectra}.
-The eigenvalue spectrum of the Hessian of the real part is different from the
-spectrum $\rho(\lambda)$ of $\partial\partial H$, but rather equivalent to the
+The eigenvalue spectrum of the Hessian of the real part is not the
+spectrum $\rho(\lambda)$ of $\partial\partial H$, but instead the
square-root eigenvalue spectrum of $(\partial\partial H)^\dagger\partial\partial H$;
in other words, the singular value spectrum $\rho(\sigma)$ of $\partial\partial
H$. When $\kappa=0$ and the elements of $J$ are standard complex normal, this
is a complex Wishart distribution. For $\kappa\neq0$ the problem changes, and
to our knowledge a closed form is not in the literature. We have worked out an
-implicit form for this spectrum using the replica method.
+implicit form for the singular value spectrum using the replica method.
Introducing replicas to bring the partition function into the numerator of the
Green function \cite{Livan_2018_Introduction} gives
@@ -263,25 +254,24 @@ Green function \cite{Livan_2018_Introduction} gives
\right]
\right\},
\end{equation}
- with sums taken over repeated Latin indices. The average is then made over
+ with sums taken over repeated Latin indices. The average is then made over
$J$ and Hubbard--Stratonovich is used to change variables to the replica matrices
- $N\alpha_{\alpha\beta}=(\zeta^{(\alpha)})^*\cdot\zeta^{(\beta)}$ and
- $N\chi_{\alpha\beta}=\zeta^{(\alpha)}\cdot\zeta^{(\beta)}$ and a series of
- replica vectors. The replica-symmetric ansatz leaves all off-diagonal
- elements and vectors zero, and
- $\alpha_{\alpha\beta}=\alpha_0\delta_{\alpha\beta}$,
+ $N\alpha_{\alpha\beta}=(\zeta^{(\alpha)})^\dagger\zeta^{(\beta)}$ and
+ $N\chi_{\alpha\beta}=(\zeta^{(\alpha)})^T\zeta^{(\beta)}$, and a series of
+ replica vectors. The replica-symmetric ansatz leaves all replica vectors
+ zero, and $\alpha_{\alpha\beta}=\alpha_0\delta_{\alpha\beta}$,
$\chi_{\alpha\beta}=\chi_0\delta_{\alpha\beta}$. The result is
\begin{equation}\label{eq:green.saddle}
\overline G(\sigma)=N\lim_{n\to0}\int d\alpha_0\,d\chi_0\,d\chi_0^*\,\alpha_0
\exp\left\{nN\left[
- 1+\frac{p(p-1)}{16}a^{p-2}\alpha_0^2-\frac{\alpha_0\sigma}2+\frac12\log(\alpha_0^2-|\chi_0|^2)
+ 1+\frac{p(p-1)}{16}r^{p-2}\alpha_0^2-\frac{\alpha_0\sigma}2+\frac12\log(\alpha_0^2-|\chi_0|^2)
+\frac p4\operatorname{Re}\left(\frac{(p-1)}8\kappa^*\chi_0^2-\epsilon^*\chi_0\right)
\right]\right\}.
\nonumber % He's too long, and we don't cite him (now)!
\end{equation}
\end{widetext}
-\begin{figure}[b]
+\begin{figure}
\centering
\includegraphics{fig/spectra_0.0.pdf}
@@ -290,25 +280,27 @@ Green function \cite{Livan_2018_Introduction} gives
\includegraphics{fig/spectra_1.5.pdf}
\caption{
- Eigenvalue and singular value spectra of the matrix $\partial\partial H$
- for $p=3$, $a=\frac54$, and $\kappa=\frac34e^{-i3\pi/4}$ with (a)
- $\epsilon=0$, (b) $\epsilon=-\frac12|\epsilon_{\mathrm{th}}|$, (c)
+ Eigenvalue and singular value spectra of the Hessian $\partial\partial H$
+ of the $3$-spin model with $\kappa=\frac34e^{-i3\pi/4}$. Pictured
+ distributions are for critical points at `radius' $r=\sqrt{5/4}$ and with
+ energy per spin (a) $\epsilon=0$, (b)
+ $\epsilon=-\frac12|\epsilon_{\mathrm{th}}|$, (c)
$\epsilon=-|\epsilon_{\mathrm{th}}|$, and (d)
- $\epsilon=-\frac32|\epsilon_{\mathrm{th}}|$. The shaded region of each inset
- shows the support of the eigenvalue distribution. The solid line on each
- plot shows the distribution of singular values, while the overlaid
- histogram shows the empirical distribution from $2^{10}\times2^{10}$ complex
- normal matrices with the same covariance and diagonal shift as
- $\partial\partial H$.
+ $\epsilon=-\frac32|\epsilon_{\mathrm{th}}|$. The shaded region of each
+ inset shows the support of the eigenvalue distribution \eqref{eq:ellipse}.
+ The solid line on each plot shows the distribution of singular values
+ \eqref{eq:spectral.density}, while the overlaid histogram shows the
+ empirical distribution from $2^{10}\times2^{10}$ complex normal matrices
+ with the same covariance and diagonal shift as $\partial\partial H$.
} \label{fig:spectra}
\end{figure}
The argument of the exponential has several saddles. The solutions $\alpha_0$
are the roots of a sixth-order polynomial, and the root with the smallest value
-of $\operatorname{Re}\alpha_0$ in all the cases we studied gives the correct
-solution. A detailed analysis of the saddle point integration is needed to
-understand why this is so. Given such $\alpha_0$, the density of singular
-values follows from the jump across the cut, or
+of $\operatorname{Re}\alpha_0$ gives the correct solution in all the cases we
+studied. A detailed analysis of the saddle point integration is needed to
+understand why this is so. Evaluated at such a solution, the density of
+singular values follows from the jump across the cut, or
\begin{equation} \label{eq:spectral.density}
\rho(\sigma)=\frac1{i\pi N}\left(
\lim_{\operatorname{Im}\sigma\to0^+}\overline G(\sigma)
@@ -318,77 +310,109 @@ values follows from the jump across the cut, or
Examples can be seen in Fig.~\ref{fig:spectra} compared with numeric
experiments.
-The transition from a one-cut to two-cut singular value spectrum naturally
-corresponds to the origin leaving the support of the eigenvalue spectrum.
-Weyl's theorem requires that the product over the norm of all eigenvalues must
-not be greater than the product over all singular values \cite{Weyl_1912_Das}.
-Therefore, the absence of zero eigenvalues implies the absence of zero singular
-values. The determination of the threshold energy -- the energy at which the
-distribution of singular values becomes gapped -- is then reduced to a
-geometry problem, and yields
+The formation of a gap in the singular value spectrum naturally corresponds to
+the origin leaving the support of the eigenvalue spectrum. Weyl's theorem
+requires that the product over the norm of all eigenvalues must not be greater
+than the product over all singular values \cite{Weyl_1912_Das}. Therefore, the
+absence of zero eigenvalues implies the absence of zero singular values. The
+determination of the threshold energy---the energy at which the distribution
+of singular values becomes gapped---is reduced to the geometry problem of
+determining when the boundary of the ellipse defined in \eqref{eq:ellipse}
+intersects the origin, and yields
\begin{equation} \label{eq:threshold.energy}
|\epsilon_{\mathrm{th}}|^2
- =\frac{p-1}{2p}\frac{(1-|\delta|^2)^2a^{p-2}}
+ =\frac{p-1}{2p}\frac{(1-|\delta|^2)^2r^{p-2}}
{1+|\delta|^2-2|\delta|\cos(\arg\kappa+2\arg\epsilon)}
\end{equation}
-for $\delta=\kappa a^{-(p-2)}$.
+for $\delta=\kappa r^{-(p-2)}$. Notice that the threshold depends on both the
+energy per spin $\epsilon$ on the `radius' $r$ of the saddle.
+
+We will now address the $\delta$-functions of \eqref{eq:complex.kac-rice}.
+These are converted to exponentials by the introduction of auxiliary fields
+$\hat z=\hat x+i\hat y$. The average over $J$ can then be performed. A
+generalized Hubbard--Stratonovich allows a change of variables from the $4N$
+original and auxiliary fields to eight bilinears defined by $Nr=z^\dagger z$,
+$N\hat r=\hat z^\dagger\hat z$, $Na=\hat z^\dagger z$, $Nb=\hat z^Tz$, and
+$N\hat c=\hat z^T\hat z$ (and their conjugates). The result, to leading order
+in $N$, is
+\begin{equation} \label{eq:saddle}
+ \overline{\mathcal N}(\kappa,\epsilon,R)
+ = \int dr\,d\hat r\,da\,da^*db\,db^*d\hat c\,d\hat c^*e^{Nf(r,\hat r,a,b,\hat c)},
+\end{equation}
+where the argument of the exponential is
+\begin{widetext}
+ \begin{equation}
+ f=2+\frac12\log\det\frac12\begin{bmatrix}
+ 1 & r & b & a \\
+ r & 1 & a^* & b^* \\
+ b & a^* & \hat c & \hat r \\
+ a & b^* & \hat r & \hat c^*
+ \end{bmatrix}
+ +\int d\lambda\,d\lambda^*\rho(\lambda)\log|\lambda|^2
+ +p\operatorname{Re}\left\{
+ \frac18\left[\hat rr^{p-1}+(p-1)|b|^2r^{p-2}+\kappa(\hat c^*+(p-1)a^2)\right]-\epsilon a
+ \right\}.
+ \end{equation}
+ The spectrum $\rho$ is given in \eqref{eq:ellipse} and is dependant on $r$ alone. This function has an
+ extremum in $\hat r$, $a$, $b$, and $\hat c$ at which its value is
+ \begin{equation} \label{eq:free.energy.a}
+ f=1+\frac12\log\left(\frac4{p^2}\frac{r^2-1}{r^{2(p-1)}-|\kappa|^2}\right)+\int d\lambda\,\rho(\lambda)\log|\lambda|^2
+ -2C_+[\operatorname{Re}(\epsilon e^{-i\theta})]^2-2C_-[\operatorname{Im}(\epsilon e^{-i\theta})]^2,
+ \end{equation}
+\end{widetext}
+where $\theta=\frac12\arg\kappa$ and
+\begin{equation}
+ C_{\pm}=\frac{r^p(1+p(r^2-1))\mp r^2|\kappa|}{r^{2p}\pm(p-1)r^p(r^2-1)|\kappa|-r^2|\kappa|^2}.
+\end{equation}
+Notice that level sets of $f$ in energy $\epsilon$ also give ellipses, but of
+different form from the ellipse in \eqref{eq:ellipse}.
-Given $\rho$, the integral in \eqref{eq:free.energy.a} may be preformed for
-arbitrary $a$. The resulting expression is maximized for $a=\infty$ for all
-values of $\kappa$ and $\epsilon$. Taking this saddle gives
+This expression is maximized for $r=R$, its value at the boundary, for
+all values of $\kappa$ and $\epsilon$. Evaluating the complexity at this
+saddle, in the limit of unbounded spins, gives
\begin{equation} \label{eq:bezout}
- \log\overline{\mathcal N}(\kappa,\epsilon)
+ \lim_{R\to\infty}\log\overline{\mathcal N}(\kappa,\epsilon,R)
=N\log(p-1).
\end{equation}
-This is, to this order, precisely the Bézout bound, the maximum number of
-solutions that $N$ equations of degree $p-1$ may have
-\cite{Bezout_1779_Theorie}. That we saturate this bound is perhaps not
-surprising, since the coefficients of our polynomial equations
-\eqref{eq:polynomial} are complex and have no symmetries. Reaching Bézout in
-\eqref{eq:bezout} is not our main result, but it provides a good check.
-Analogous asymptotic scaling has been found for the number of pure Higgs states
-in supersymmetric quiver theories \cite{Manschot_2012_From}.
-
-More insight is gained by looking at the count as a function of $a$, defined by
-$\overline{\mathcal N}(\kappa,\epsilon,a)=e^{Nf(a)}$. In the large-$N$ limit,
-this is the cumulative number of critical points, or the number of critical
-points $z$ for which $|z|^2\leq a$. We likewise define the $a$-dependant
-complexity $\Sigma(\kappa,\epsilon,a)=N\log\overline{\mathcal
-N}(\kappa,\epsilon,a)$
+This is, to leading order, precisely the Bézout bound, the maximum number of
+solutions to $N$ equations of degree $p-1$ \cite{Bezout_1779_Theorie}. That we
+saturate this bound is perhaps not surprising, since the coefficients of our
+polynomial equations \eqref{eq:polynomial} are complex and have no symmetries.
+Reaching Bézout in \eqref{eq:bezout} is not our main result, but it provides a
+good check. Analogous asymptotic scaling has been found for the number of pure
+Higgs states in supersymmetric quiver theories \cite{Manschot_2012_From}.
\begin{figure}[htpb]
\centering
\includegraphics{fig/complexity.pdf}
\caption{
- The complexity of the 3-spin model at $\epsilon=0$ as a function of
- $a=|z|^2=1+y^2$ at several values of $\kappa$. The dashed line shows
+ The complexity of the 3-spin model as a function of the maximum `radius'
+ $R$ at zero energy and several values of $\kappa$. The dashed line shows
$\frac12\log(p-1)$, while the dotted shows $\log(p-1)$.
} \label{fig:complexity}
\end{figure}
-Everything is analytically tractable for $\epsilon=0$, giving
+For finite $R$, everything is analytically tractable at $\epsilon=0$:
\begin{equation} \label{eq:complexity.zero.energy}
- \Sigma(\kappa,0,a)
- =\log(p-1)-\frac12\log\left(\frac{1-|\kappa|^2a^{-2(p-1)}}{1-a^{-2}}\right).
+ \Sigma(\kappa,0,R)
+ =\log(p-1)-\frac12\log\left(\frac{1-|\kappa|^2R^{-4(p-1)}}{1-R^{-4}}\right).
\end{equation}
-Notice that the limit of this expression as $a\to\infty$ corresponds with
-\eqref{eq:bezout}, as expected. This is plotted as a function of $a$ for
-several values of $\kappa$ in Fig.~\ref{fig:complexity}. For any $|\kappa|<1$,
-the complexity goes to negative infinity as $a\to1$, i.e., as the spins are
-restricted to the reals. This is natural, given that the $y$ contribution to
-the volume shrinks to zero as that of an $N$-dimensional sphere $\sum_i y_i^2=N(a-1)$ with volume
-$\sim(a-1)^N$. However, when the result is analytically continued to
+This is plotted as a function of $R$ for several values of $\kappa$ in
+Fig.~\ref{fig:complexity}. For any $|\kappa|<1$, the complexity goes to
+negative infinity as $R\to1$, i.e., as the spins are restricted to the reals.
+This is natural, since volume of configuration space vanishes in this limit
+like $(R^2-1)^N$. However, when the result is analytically continued to
$\kappa=1$ (which corresponds to real $J$) something novel occurs: the
-complexity has a finite value at $a=1$. Since the $a$-dependence gives a
-cumulative count, this implies a $\delta$-function density of critical points
-along the line $y=0$. The number of critical points contained within is
+complexity has a finite value at $R=1$. This implies a $\delta$-function
+density of critical points on the $r=1$ (or $y=0$) boundary. The number of
+critical points contained there is
\begin{equation}
- \lim_{a\to1}\lim_{\kappa\to1}\log\overline{\mathcal N}(\kappa,0,a)
+ \lim_{R\to1}\lim_{\kappa\to1}\log\overline{\mathcal N}(\kappa,0,R)
= \frac12N\log(p-1),
\end{equation}
half of \eqref{eq:bezout} and corresponding precisely to the number of critical
-points of the real $p$-spin model (note the role of conjugation symmetry,
-already underlined in \cite{Bogomolny_1992_Distribution}). The full
+points of the real $p$-spin model. (Note the role of conjugation symmetry,
+already underlined in \cite{Bogomolny_1992_Distribution}.) The full
$\epsilon$-dependence of the real $p$-spin is recovered by this limit as
$\epsilon$ is varied.
@@ -396,23 +420,35 @@ $\epsilon$ is varied.
\centering
\includegraphics{fig/desert.pdf}
\caption{
- The minimum value of $a$ for which the complexity is positive as a function
- of (real) energy $\epsilon$ for the 3-spin model at several values of
- $\kappa$.
+ The value of bounding `radius' $R$ for which $\Sigma(\kappa,\epsilon,R)=0$ as a
+ function of (real) energy per spin $\epsilon$ for the 3-spin model at
+ several values of $\kappa$. Above each line the complexity is positive and
+ critical points proliferate, while below it the complexity is negative and
+ critical points are exponentially suppressed. The dotted black lines show
+ the location of the ground and highest exited state energies for the real
+ 3-spin model.
} \label{fig:desert}
\end{figure}
+In the thermodynamic limit, \eqref{eq:complexity.zero.energy} implies that most
+critical points are concentrated at infinite radius $r$. For finite $N$ the
+average radius of critical points is likewise finite. By differentiating
+$\overline{\mathcal N}$ with respect to $R$ and normalizing, one obtains the
+distribution of critical points as a function of $r$. This yields an average
+radius proportional to $N^{1/4}$. One therefore expects typical critical
+points to have a norm that grows modestly with system size.
+
These qualitative features carry over to nonzero $\epsilon$. In
-Fig.~\ref{fig:desert} we show that for $\kappa<1$ there is always a gap of $a$
-close to one for which there are no solutions. When $\kappa=1$---the analytic
-continuation to the real computation---the situation is more interesting. In
-the range of energies where there are real solutions this gap closes, which is
-only possible if the density of solutions diverges at $a=1$. Another
-remarkable feature of this limit is that there is still a gap without solutions
-around `deep' real energies where there is no real solution. A moment's thought
-tells us that this is a necessity: otherwise a small perturbation of the $J$s
-could produce an unusually deep solution to the real problem, in a region where
-this should not happen.
+Fig.~\ref{fig:desert} we show that for $\kappa<1$ there is always a gap in $r$
+close to one in which solutions are exponentially suppressed. When
+$\kappa=1$---the analytic continuation to the real computation---the situation
+is more interesting. In the range of energies where there are real solutions
+this gap closes, which is only possible if the density of solutions diverges at
+$r=1$. Outside this range, around `deep' real energies where real solutions are
+exponentially suppressed, the gap remains. A moment's thought tells us that
+this is necessary: otherwise a small perturbation of the $J$s could produce
+an unusually deep solution to the real problem, in a region where this should
+not happen.
\begin{figure}[t]
\centering
@@ -425,37 +461,38 @@ this should not happen.
\caption{
Energies at which states exist (green shaded region) and threshold energies
(black solid line) for the 3-spin model with
- $\kappa=\frac34e^{-i3\pi/4}$ and (a) $a=2$, (b) $a=1.325$, (c) $a=1.125$,
- and (d) $a=1$. No shaded region is shown in (d) because no states exist at
+ $\kappa=\frac34e^{-i3\pi/4}$ and (a) $r=\sqrt2$, (b) $r=\sqrt{1.325}$, (c) $r=\sqrt{1.125}$,
+ and (d) $r=1$. No shaded region is shown in (d) because no states exist at
any energy.
} \label{fig:eggs}
\end{figure}
The relationship between the threshold and ground, or extremal, state energies
-is richer than in the real case. In Fig.~\ref{fig:eggs} these are shown in the
+is richer than in the real case. In Fig.~\ref{fig:eggs} these are shown in the
complex-$\epsilon$ plane for several examples. Depending on the parameters, the
-threshold might always come at smaller magnitude than the extremal state, or
-always come at larger magnitude, or cross as a function of complex argument.
-For sufficiently large $a$ the threshold always comes at larger magnitude than
-the extremal state. If this were to happen in the real case, it would likely
-imply our replica symmetric computation is unstable, since having a ground
-state above the threshold implies a ground state Hessian with many negative
-eigenvalues, a contradiction. However, this is not an obvious contradiction in
-the complex case. The relationship between the threshold, i.e., where the gap
-appears, and the dynamics of, e.g., a minimization algorithm or physical
-dynamics, are a problem we hope to address in future work.
-
- This paper provides a first step towards the study of a complex landscape with
- complex variables. The next obvious one is to study the topology of the
+threshold might have a smaller or larger magnitude than the extremal state, or
+cross as a function of complex argument. For sufficiently large $r$ the
+threshold is always at a larger magnitude. If this were to happen in the real
+case, it would likely imply our replica symmetric computation were unstable,
+since having a ground state above the threshold implies a ground state Hessian
+with many negative eigenvalues, a contradiction. However, this is not an
+contradiction in the complex case, where the energy is not bounded from below.
+The relationship between the threshold, i.e., where the gap appears, and the
+dynamics of, e.g., a minimization algorithm, deformed integration cycle, or
+physical dynamics, are a problem we hope to address in future work.
+
+ This paper provides a first step towards the study of complex landscapes with
+ complex variables. The next obvious step is to study the topology of the
critical points, the sets reached following gradient descent (the
Lefschetz thimbles), and ascent (the anti-thimbles) \cite{Witten_2010_A,
Witten_2011_Analytic, Cristoforetti_2012_New, Behtash_2017_Toward,
Scorzato_2016_The}, which act as constant-phase integrating `contours.'
Locating and counting the saddles that are joined by gradient lines---the
Stokes points, which play an important role in the theory---is also well within
- reach of the present-day spin-glass literature techniques. We anticipate
- that the threshold level, where the system develops a mid-spectrum gap, will
- play a crucial role as it does in the real case.
+ reach of the present-day spin-glass literature techniques. We anticipate
+ that the threshold level, where the system develops a mid-spectrum gap, plays
+ a crucial role in determining whether these Stokes points proliferate under
+ some continuous change of parameters.
\begin{acknowledgments}
We wish to thank Alexander Altland, Satya Majumdar and Gregory Schehr for a useful suggestions.
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+ Charting`ScaledFrameTicks[{Identity, Identity}]}, {
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+ ImagePadding -> Automatic,
+ BaselinePosition -> (Scaled[0.1] -> Baseline)], #}, {
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+ ImageSize -> {20, 10}, PlotRangePadding -> None,
+ ImagePadding -> Automatic,
+ BaselinePosition -> (Scaled[0.1] -> Baseline)], #3}, {
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+ ImageSize -> {20, 10}, PlotRangePadding -> None,
+ ImagePadding -> Automatic,
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+ GrayLevel[0]]],
+ PointSize[0.5],
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+ AbsoluteThickness[1.6]], {}}}, AspectRatio -> Full,
+ ImageSize -> {20, 10}, PlotRangePadding -> None,
+ ImagePadding -> Automatic,
+ BaselinePosition -> (Scaled[0.1] -> Baseline)], #5}},
+ GridBoxAlignment -> {
+ "Columns" -> {Center, Left}, "Rows" -> {{Baseline}}},
+ AutoDelete -> False,
+ GridBoxDividers -> {
+ "Columns" -> {{False}}, "Rows" -> {{False}}},
+ GridBoxItemSize -> {
+ "Columns" -> {{All}}, "Rows" -> {{All}}},
+ GridBoxSpacings -> {
+ "Columns" -> {{0.5}}, "Rows" -> {{0.8}}}], "Grid"]}},
+ GridBoxAlignment -> {
+ "Columns" -> {{Left}}, "Rows" -> {{Top}}}, AutoDelete ->
+ False, GridBoxItemSize -> {
+ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}},
+ GridBoxSpacings -> {"Columns" -> {{1}}, "Rows" -> {{0}}}],
+ "Grid"]}}, GridBoxAlignment -> {"Columns" -> {{Center}}},
+ AutoDelete -> False,
+ GridBoxItemSize -> {
+ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}},
+ GridBoxSpacings -> {
+ "Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Grid"],
+ Alignment -> Left, AppearanceElements -> None,
+ ImageMargins -> {{5, 5}, {5, 5}}, ImageSizeAction ->
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+ RectangleBox[{0, -1}, {2, 1}]}}, DefaultBaseStyle ->
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+ FrameStyle ->
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+ 0.4731986666666667], FrameTicks -> None, PlotRangePadding ->
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+ Dynamic[{
+ Automatic,
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+ BaseStyle -> {}, BaselinePosition -> Baseline,
+ DefaultBaseStyle -> {}, ButtonFunction :>
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+
diff --git a/referee_respose.txt b/referee_respose.txt
new file mode 100644
index 0000000..3b204c6
--- /dev/null
+++ b/referee_respose.txt
@@ -0,0 +1,158 @@
+----------------------------------------------------------------------
+Response to Referee A -- LZ16835/Kent-Dobias
+----------------------------------------------------------------------
+
+Referee A wrote:
+> The authors consider the mean-field p-spin spherical model with
+> *complex* variables and study the number of saddle points of the
+> energy and the eigenvalue distribution of their Hessian matrix. The
+> main result of the rather technical computation is that in a
+> particular limit (concretely kappa->1) the known results for the real
+> p-spin spherical model are reproduced, the (expected) Bézout bound for
+> the number of solutions of the saddle point equations is reached and
+> that the relationships between the “threshold” and extremal state
+> energies is richer in the complex case than in the real case.
+>
+> I must admit that I was not able to grasp any far-reaching
+> consequences of the computational tour de force only hinted at in the
+> manuscript, and I fear that a nonexpert reader would also not be able
+> to do so. Two arguments are pushed forward by the authors to justify
+> the dissemination of their results to the broader readership of PRL:
+> One is that there are indeed situations in which complex variables
+> appear naturally in disordered system. The first example the authors
+> mention is a Hamiltonian that could be relevant for with random Laser
+> problems and was analyzed 2015 in PRA, which has up to now 30
+> citations according to Google Scholar, and the second example is a
+> Hamiltonian from sting theory that was analyzed in 2016 in JHEP, which
+> has up to now 31 citations. I do not feel that these two examples
+> prove that the enumeration of saddle points if the p-spin model is
+> important or of broad interest.
+>
+> The second argument of the authors is that extending a real problem to
+> the complex plane often uncovers underlying simplicity that is
+> otherwise hidden, shedding light on the original real problem. Here I
+> come back to what I already mentioned above: I do not see any
+> simplicity emerging from the present calculation and I also do not see
+> the original problem in a new light. Therefore, I do not think that
+> one of the four PRL criteria is actually fulfilled and I recommend to
+> transfer the manuscript to PRE.
+
+We disagree with the referee's assessment here, as we have also explained in
+our letter to the editors. Something in particular that goes unaddressed is
+another motivation (which in the referee's defense we did not enumerate clearly
+in our draft): that understanding the distribution of complex critical points
+is necessary in the treatment of a large class of integrals involved both in
+the definition of quantum mechanics with a complex action and in ameliorating
+the sign problem in, e.g., lattice QCD.
+
+If the criteria for publication is to be "first past the post" of cited
+citations, one might examine our citations of that literature:
+
+ - Analytic continuation of Chern-Simons theory, E Witten (2011): 444 citations
+
+ - New approach to the sign problem in quantum field theories: High density
+ QCD on a Lefschetz thimble, M Cristoforetti et al (2012): 285 citations
+
+Both works are concerned with the location and relative positions of critical
+points of complex theories. In the resubmitted manuscript we have better
+emphasized this motivation.
+
+Referee A wrote:
+> Although the first part of the manuscript is well written and well
+> understandable (at least for me) from page 2 on it becomes very
+> technical and unreadable for a non-expert. If the reader skips to the
+> results and tries to understand the figures she/he is left with the
+> ubiquitous parameter a, whose physical meaning is hidden deep in the
+> saddle point calculation (“dictates the magnitude of |z|^2” – well,
+> with respect to the solutions of (3): is “a” the average value of the
+> modulus squared of the solution z’s or not?). Similar with epsilon:
+> apparently it is the average energy of the saddle point solution – why
+> not writing so also in the figure captions? The paper would profit a
+> lot from a careful rewriting of at least the result section and to
+> provide figure captions with the physical meaning of the quantities
+> and parameters shown.
+
+We thank the referee for their helpful suggestions with regards to the
+readability of our manuscript. In the resubmitted version, much has been
+rewritten for clarity. We would like to highlight several of the most
+substantive changes:
+
+ - The ubiquitous parameter 'a' was replaced by the more descriptive 'r^2', as
+ it is a sort of radius, along with a new parameter 'R^2' which bounds it.
+ Descriptions in English of these were added to the figure captions.
+
+ - The technical portion of the paper was reordered to connect better with the
+ sections preceding and following it.
+
+ - The location of the results is now indicated before the beginning of the
+ technical portion for readers interested in skipping ahead.
+
+Referee A wrote:
+> A couple of minor, technical, quibbles:
+>
+> 1) If there is any real world application of a p-spin model with
+> complex variables it will NOT have a spherical constraint. I would
+> suggest to discuss the consequences of this constraint, which is
+> introduced for computational simplicity.
+>
+> 2) After eq. (2): ”We choose to constrain our model by z^2=N.“ Then it
+> is not a spherical constraint any more – does it have any physical
+> relevance?
+
+We have added a more detailed discussion of the constraint to address these
+confusions, emphasizing its purpose. The new paragraphs are:
+
+> One might balk at the constraint $z^Tz=N$---which could appropriately be
+> called a \emph{hyperbolic} constraint---by comparison with $z^\dagger z=N$.
+> The reasoning behind the choice is twofold.
+>
+> First, we seek draw conclusions from our model that are applicable to generic
+> holomorphic functions without any symmetry. Samples of $H_0$ nearly provide
+> this, save for a single anomaly: the value of the energy and its gradient at
+> any point $z$ correlate along the $z$ direction, with $\overline{H_0\partial
+> H_0}\propto \overline{H_0(\partial H_0)^*}\propto z$. This anomalous
+> direction should thus be forbidden, and the constraint surface $z^Tz=N$
+> accomplishes this.
+>
+> Second, taking the constraint to be the level set of a holomorphic function
+> means the resulting configuration space is a \emph{bone fide} complex
+> manifold, and therefore permits easy generalization of the integration
+> techniques referenced above. The same cannot be said for the space defined by
+> $z^\dagger z=N$, which is topologically the $(2N-1)$-sphere and cannot admit
+> a complex structure.
+>
+> Imposing the constraint with a holomorphic function makes the resulting
+> configuration space a \emph{bone fide} complex manifold, which is, as we
+> mentioned, the situation we wish to model. The same cannot be said for the
+> space defined by $z^\dagger z=N$, which is topologically the $(2N-1)$-sphere,
+> does not admit a complex structure, and thus yields a trivial structure of
+> saddles. However, we will introduce the bound $r^2\equiv z^\dagger z/N\leq
+> R^2$ on the `radius' per spin as a device to classify saddles. We shall see
+> that this `radius' $r$ and its upper bound $R$ are insightful knobs in our
+> present problem, revealing structure as they are varied. Note that taking
+> $R=1$ reduces the problem to that of the ordinary $p$-spin.
+
+Referee A wrote:
+> 3) On p.2: “…a, which dictates the magnitude of |z|^2, or
+> alternatively the magnitude y^2 of the imaginary part. The last part
+> is hard to understand, should be explained.
+
+We thank the referee for pointing out this confusing statement, which was
+unnecessary and removed.
+
+> 4) On p.2: “In most the parameter space we shall study her, the
+> annealed approximation is exact.” I think it is necessary to provide
+> some evidence her, because the annealed approximation is usually a
+> pretty severe approximation.
+
+We have nuanced the statement in question and added a citation to a review
+article which outlines the reasoning for analogous models. The amended sentence
+reads:
+
+> Based on the experience from similar problems \cite{Castellani_2005_Spin-glass},
+> the \emph{annealed approximation} $N\Sigma\sim\log\overline{\mathcal N}$ is
+> expected to be exact wherever the complexity is positive.
+
+Sincerely,
+Jaron Kent-Dobias & Jorge Kurchan
+
diff --git a/referee_respose_2.txt b/referee_respose_2.txt
new file mode 100644
index 0000000..4ddc8e3
--- /dev/null
+++ b/referee_respose_2.txt
@@ -0,0 +1,21 @@
+The latest reviews had nothing to say about the scientific content of our
+paper, only about its relevance to Physical Review Letters. For resubmission to
+Physical Review Research, we have therefore changed nothing except to expand on
+our reasoning regarding the relevance of this work to the broader physics
+community, and indeed across disciplines. The end of the fourth paragraph now
+reads:
+
+> [...] In order to do this correctly, features of the action's landscape in
+> complex space---such as the relative position of saddles and the existence of
+> Stokes lines joining them---must be understood. This is typically done for
+> simple actions with few saddles, or for a target phenomenology with
+> symmetries that restrict the set of saddles to few candidates. Given the
+> recent proliferation of `glassiness' in condensed matter and high energy
+> physics, it is inevitable that someone will want to apply these methods to a
+> system with a complex landscape, and will find they cannot use approaches
+> that rely on such assumptions. Their landscape may not be random: here we
+> follow the standard strategy of computer science by understanding the generic
+> features of random instances of a simple case, expecting that this sheds
+> light on practical, nonrandom problems. While in this paper we do not yet
+> address analytic continuation of integrals, understanding the distribution
+> and spectra of critical points is an essential first step.