\documentclass[reprint,aps,prl,longbibliography,floatfix]{revtex4-2} \usepackage[utf8]{inputenc} % why not type "Bézout" with unicode? \usepackage[T1]{fontenc} % vector fonts plz \usepackage{amsmath,amssymb,latexsym,graphicx} \usepackage{newtxtext,newtxmath} % Times for PR \usepackage[dvipsnames]{xcolor} \usepackage[ colorlinks=true, urlcolor=MidnightBlue, citecolor=MidnightBlue, filecolor=MidnightBlue, linkcolor=MidnightBlue ]{hyperref} % ref and cite links with pretty colors \usepackage{anyfontsize} \begin{document} \title{ Unveiling the complexity of hierarchical energy landscapes } \author{Jaron Kent-Dobias} \author{Jorge Kurchan} \affiliation{Laboratoire de Physique de l'Ecole Normale Supérieure, Paris, France} \begin{abstract} Complexity is a measure of the number of stationary points in complex landscapes. We derive a general solution for the complexity of mean-field complex landscapes. It incorporates Parisi's solution for the ground state, as it should. Using this solution, we count the stationary points of two models: one with multi-step replica symmetry breaking, and one with full replica symmetry breaking. These examples demonstrate the consistency of the solution and reveal that the signature of replica symmetry breaking at high energy densities is found in high-index saddles, not minima. \end{abstract} \maketitle The functions used to describe the energies, costs, and fitnesses of disordered systems in physics, computer science, and biology are typically \emph{complex}, meaning that they have a number of minima that grows exponentially with the size of the system \cite{Maillard_2020_Landscape, Ros_2019_Complex, Altieri_2021_Properties}. Though they are often called `rough landscapes' to evoke the intuitive image of many minima in something like a mountain range, the metaphor to topographical landscapes is strained by the reality that these complex landscapes also exist in very high dimensions: think of the dimensions of phase space for $N$ particles, or the number of parameters in a neural network. The \emph{complexity} of a function is the average of the logarithm of the number of its minima, maxima, and saddle points (collectively stationary points), under conditions like the value of the energy or the index of the stationary point \cite{Bray_1980_Metastable}. Since in complex landscapes this number grows exponentially with system size, their complexity is an extensive quantity. Understanding the complexity offers an understanding about the geometry and topology of the landscape, which can provide insight into dynamical behavior. When complex systems are fully connected, i.e., each degree of freedom interacts directly with every other, they are often described by a hierarchical structure of the type first proposed by Parisi, the \emph{replica symmetry breaking} (RSB) \cite{Parisi_1979_Infinite}. This family of structures is rich, spanning uniform \emph{replica symmetry} (RS), an integer $k$ levels of hierarchical nested structure ($k$RSB), a full continuum of nested structure (full RSB or FRSB), and arbitrary combinations thereof. Though these rich structures are understood in the equilibrium properties of fully connected models, the complexity has only been computed in RS cases. In this paper and its longer companion, we share the first results for the complexity with nontrivial hierarchy \cite{Kent-Dobias_2022_How}. Using a general form for the solution detailed in a companion article, we describe the structure of landscapes with a 1RSB complexity and a full RSB complexity \footnote{The Thouless--Anderson--Palmer (TAP) complexity is the complexity of a kind of mean-field free energy. Because of some deep thermodynamic relationships between the TAP complexity and the equilibrium free energy, the TAP complexity can be computed with extensions of the equilibrium method. As a result, the TAP complexity has been previously computed for nontrivial hierarchical structure.}. We study the mixed $p$-spin spherical models, with Hamiltonian \begin{equation} \label{eq:hamiltonian} H(\mathbf s)=-\sum_p\frac1{p!}\sum_{i_1\cdots i_p}^NJ^{(p)}_{i_1\cdots i_p}s_{i_1}\cdots s_{i_p} \end{equation} is defined for vectors $\mathbf s\in\mathbb R^N$ confined to the $N-1$ sphere $S^{N-1}=\{\mathbf s\mid\|\mathbf s\|^2=N\}$. The coupling coefficients $J$ are taken at random, with zero mean and variance $\overline{(J^{(p)})^2}=a_pp!/2N^{p-1}$ chosen so that the energy is typically extensive. The overbar will always denote an average over the coefficients $J$. The factors $a_p$ in the variances are freely chosen constants that define the particular model. For instance, the so-called `pure' models have $a_p=1$ for some $p$ and all others zero. The complexity of the $p$-spin models has been extensively studied by physicists and mathematicians. Among physicists, the bulk of work has been on the so-called Thouless--Anderson--Palmer (TAP) complexity for the pure models, which counts minima in a kind of mean-field free energy \cite{Rieger_1992_The, Crisanti_1995_Thouless-Anderson-Palmer, Cavagna_1997_An, Cavagna_1997_Structure, Cavagna_1998_Stationary, Cavagna_2005_Cavity, Giardina_2005_Supersymmetry}. The landscape complexity has been proven for pure and mixed models without RSB \cite{Auffinger_2012_Random, Auffinger_2013_Complexity, BenArous_2019_Geometry}. The mixed models been treated in specific cases, again without RSB \cite{Folena_2020_Rethinking, Ros_2019_Complex}. And the methods of complexity have been used to study many geometric properties of the pure models, from the relative position of stationary points to one another to shape and prevalence of instantons \cite{Ros_2019_Complexity, Ros_2021_Dynamical}. The variance of the couplings implies that the covariance of the energy with itself depends on only the dot product (or overlap) between two configurations. In particular, one finds \begin{equation} \label{eq:covariance} \overline{H(\mathbf s_1)H(\mathbf s_2)}=Nf\left(\frac{\mathbf s_1\cdot\mathbf s_2}N\right) \end{equation} where $f$ is defined by the series \begin{equation} f(q)=\frac12\sum_pa_pq^p \end{equation} One needn't start with a Hamiltonian like \eqref{eq:hamiltonian}, defined as a series: instead, the covariance rule \eqref{eq:covariance} can be specified for arbitrary, non-polynomial $f$, as in the `toy model' of M\'ezard and Parisi \cite{Mezard_1992_Manifolds}. In fact, defined this way the mixed spherical model encompasses all isotropic Gaussian fields on the sphere. The family of spherical models thus defined is quite rich, and by varying the covariance $f$ nearly any hierarchical structure can be found in equilibrium. Because of a correspondence between the ground state complexity and the entropy at zero temperature, any hierarchical structure in the equilibrium should be reflected in the complexity. The complexity is calculated using the Kac--Rice formula, which counts the stationary points using a $\delta$-function weighted by a Jacobian \cite{Kac_1943_On, Rice_1939_The}. The count is given by \begin{equation} \begin{aligned} \mathcal N(E, \mu) &=\int_{S^{N-1}}d\mathbf s\, \delta\big(\nabla H(\mathbf s)\big)\,\big|\det\operatorname{Hess}H(\mathbf s)\big| \\ &\hspace{2pc}\times\delta\big(NE-H(\mathbf s)\big)\delta\big(N\mu-\operatorname{Tr}\operatorname{Hess}H(\mathbf s)\big) \end{aligned} \end{equation} with two additional $\delta$-functions inserted to fix the energy density $E$ and the stability $\mu$. The complexity is then \begin{equation} \label{eq:complexity} \Sigma(E,\mu)=\lim_{N\to\infty}\frac1N\overline{\log\mathcal N(E, \mu}). \end{equation} Most of the difficulty of this calculation resides in the logarithm in this formula. The stability $\mu$, sometimes called the radial reaction, determines the depth of minima or the index of saddles. At large $N$ the Hessian can be shown to consist of the sum of a GOE matrix with variance $f''(1)/N$ shifted by a constant diagonal matrix of value $\mu$. Therefore, the spectrum of the Hessian is a Wigner semicircle of radius $\mu_m=\sqrt{4f''(1)}$ centered at $\mu$. When $\mu>\mu_m$, stationary points are minima whose sloppiest eigenvalue is $\mu-\mu_m$. When $\mu=\mu_m$, the stationary points are marginal minima with flat directions. When $\mu<\mu_m$, the stationary points are saddles with indexed fixed to within order one (fixed macroscopic index). It's worth reviewing the complexity for the best-studied case of the pure model for $p\geq3$ \cite{Cugliandolo_1993_Analytical}. Here, because the covariance is a homogeneous polynomial, $E$ and $\mu$ cannot be fixed separately, and one implies the other: $\mu=pE$. Therefore at each energy there is only one kind of stationary point. When the energy reaches $E_\mathrm{th}=-\mu_m/p$, the population of stationary points suddenly shifts from all saddles to all minima, and there is an abrupt percolation transition in the topology of constant-energy slices of the landscape. This behavior of the complexity can be used to explain a rich variety of phenomena in the equilibrium and dynamics of the pure models: the threshold energy $E_\mathrm{th}$ corresponds to the average energy at the dynamic transition temperature, and the asymptotic energy reached by slow aging dynamics. Things become much less clear in even the simplest mixed models. For instance, one mixed model known to have a replica symmetric complexity was shown to nonetheless not have a clear relationship between features of the complexity and the asymptotic dynamics \cite{Folena_2020_Rethinking}. There is no longer a sharp topological transition. In the pure models, $E_\mathrm{th}$ also corresponds to the \emph{algorithmic threshold} $E_\mathrm{alg}$, defined by the lowest energy reached by local algorithms like approximate message passing \cite{ElAlaoui_2020_Algorithmic, ElAlaoui_2021_Optimization}. In the spherical models, this has been proven to be \begin{equation} E_{\mathrm{alg}}=-\int_0^1dq\,\sqrt{f''(q)} \end{equation} For full RSB systems, $E_\mathrm{alg}=E_0$ and the algorithm can reach the ground state energy. For the pure $p$-spin models, $E_\mathrm{alg}=E_\mathrm{th}$, where $E_\mathrm{th}$ is the energy at which marginal minima are the most common stationary points. Something about the topology of the energy function might be relevant to where this algorithmic threshold lies. To compute the complexity in the generic case, we use the replica method to treat the logarithm inside the average of \eqref{eq:complexity}, and the $\delta$-functions are written in a Fourier basis. The average of the factor including the determinant and the factors involving $\delta$-functions can be averaged over the disorder separately \cite{Bray_2007_Statistics}. The result can be written as a function of three matrices indexed by the replicas: one which is a clear analogue of the usual overlap matrix of the equilibrium case, and two which can be related to the response of stationary points to perturbations of the potential. The general expression for the complexity as a function of these matrices is also found in \cite{Folena_2020_Rethinking}. We make the \emph{ansatz} that all three matrices have a hierarchical structure, and moreover that they share the same hierarchical structure. This means that the size of the blocks of equal value of each is the same, though the values inside these blocks will vary from matrix to matrix. This form can be shown to exactly reproduce the ground state energy predicted by the equilibrium solution, a key consistency check. Along one line in the energy--stability plane the solution takes a simple form: the two hierarchical matrices corresponding to responses are diagonal, leaving only the overlap matrix with nontrivial off-diagonal entries. This simplification makes the solution along this line analytically tractable even for FRSB. The simplification is related to the presence of an approximate supersymmetry in the Kac--Rice formula, studied in the past in the context of the TAP free energy. This line of `supersymmetric' solutions terminates at the ground state, and describes the most numerous types of stable minima. Using this solution, one finds a correspondence between properties of the overlap matrix at the ground state energy, where the complexity vanishes, and the overlap matrix in the equilibrium problem in the limit of zero temperature. The saddle point parameters of the two problems are related exactly. In the case where the vicinity of the equilibrium ground state is described by a $k$RSB solution, the complexity at the ground state is $(k-1)$RSB. This can be intuitively understood by considering the difference between measuring overlaps between equilibrium \emph{states} and stationary \emph{points}. For states, the finest level of the hierarchical description gives the typical overlap between two points drawn from the same state, which has some distribution about the ground state at nonzero temperature. For points, this finest level does not exist. In general, solving the saddle-point equations for the parameters of the three replica matrices is challenging. Unlike the equilibrium case, the solution is not extremal, and so minimization methods cannot be used. However, the line of simple `supersymmetric' solutions offers a convenient foothold: starting from one of these solutions, the parameters $E$ and $\mu$ can be slowly varied to find the complexity everywhere. This is how the data in what follows was produced. \begin{figure} \centering \hspace{-1em} \includegraphics[width=\columnwidth]{figs/316_complexity_contour_1_letter.pdf} \caption{ Complexity of the $3+16$ model in the energy $E$ and stability $\mu$ plane. The right shows a detail of the left. Below the yellow marginal line the complexity counts saddles of increasing index as $\mu$ decreases. Above the yellow marginal line the complexity counts minima of increasing stability as $\mu$ increases. } \label{fig:2rsb.contour} \end{figure} \begin{figure} \centering \includegraphics[width=\columnwidth]{figs/316_detail_letter.pdf} \includegraphics[width=\columnwidth]{figs/316_detail_letter_legend.pdf} \caption{ Detail of the `phases' of the $3+16$ model complexity as a function of energy and stability. Above the yellow marginal stability line the complexity counts saddles of fixed index, while below that line it counts minima of fixed stability. The shaded red region shows places where the complexity is described by the 1RSB solution, while the shaded gray region shows places where the complexity is described by the RS solution. In white regions the complexity is zero. Several interesting energies are marked with vertical black lines: the traditional `threshold' $E_\mathrm{th}$ where minima become most numerous, the algorithmic threshold $E_\mathrm{alg}$ that bounds the performance of smooth algorithms, and the average energies at the $2$RSB and $1$RSB equilibrium transitions $\langle E\rangle_2$ and $\langle E\rangle_1$, respectively. Though the figure is suggestive, $E_\mathrm{alg}$ lies at slightly lower energy than the termination of the RS -- 1RSB transition line. } \label{fig:2rsb.phases} \end{figure} For the first example, we study a model whose complexity has the simplest replica symmetry breaking scheme, 1RSB. By choosing a covariance $f$ as the sum of polynomials with well-separated powers, one develops 2RSB in equilibrium. This should correspond to 1RSB in the complexity. We take \begin{equation} f(q)=\frac12\left(q^3+\frac1{16}q^{16}\right) \end{equation} established to have a 2RSB ground state \cite{Crisanti_2011_Statistical}. With this covariance, the model sees a replica symmetric to 1RSB transition at $\beta_1=1.70615\ldots$ and a 1RSB to 2RSB transition at $\beta_2=6.02198\ldots$. The typical equilibrium energies at these phase transitions are listed in Table~\ref{tab:energies}. \begin{table} \begin{tabular}{l|cc} & $3+16$ & $2+4$ \\\hline\hline $\langle E\rangle_\infty$ &---& $-0.531\,25\hphantom{1\,111\dots}$ \\ $\hphantom{\langle}E_\mathrm{max}$ & $-0.886\,029\,051\dots$ & $-1.039\,701\,412\dots$\\ $\langle E\rangle_1$ & $-0.906\,391\,055\dots$ & ---\\ $\langle E\rangle_2$ & $-1.195\,531\,881\dots$ & ---\\ $\hphantom{\langle}E_\mathrm{dom}$ & $-1.273\,886\,852\dots$ & $-1.056\,6\hphantom{11\,111\dots}$\\ $\hphantom{\langle}E_\mathrm{alg}$ & $-1.275\,140\,128\dots$ & $-1.059\,384\,319\ldots$\\ $\hphantom{\langle}E_\mathrm{th}$ & $-1.287\,575\,114\dots$ & $-1.059\,384\,319\ldots$\\ $\hphantom{\langle}E_\mathrm{m}$ & $-1.287\,605\,527\ldots$ & $-1.059\,384\,319\ldots$ \\ $\hphantom{\langle}E_0$ & $-1.287\,605\,530\ldots$ & $-1.059\,384\,319\ldots$\\\hline \end{tabular} \caption{ Landmark energies of the equilibrium and complexity problems for the two models studied. $\langle E\rangle_1$, $\langle E\rangle_2$ and $\langle E\rangle_\infty$ are the average energies in equilibrium at the RS--1RSB, 1RSB--2RSB, and RS--FRSB transitions, respectively. $E_\mathrm{max}$ is the highest energy at which any stationary points are described by a RSB complexity. $E_\mathrm{dom}$ is the energy at which dominant stationary points have an RSB complexity. $E_\mathrm{alg}$ is the algorithmic threshold below which smooth algorithms cannot go. $E_\mathrm{th}$ is the traditional threshold energy, defined by the energy at which marginal minima become most common. $E_\mathrm m$ is the lowest energy at which saddles or marginal minima are found. $E_0$ is the ground state energy. } \label{tab:energies} \end{table} In this model, the RS complexity gives an inconsistent answer for the complexity of the ground state, predicting that the complexity of minima vanishes at a higher energy than the complexity of saddles, with both at a lower energy than the equilibrium ground state. The 1RSB complexity resolves these problems, predicting the same ground state as equilibrium and with a ground state stability $\mu_0=6.480\,764\ldots>\mu_\mathrm m$. It predicts that the complexity of marginal minima (and therefore all saddles) vanishes at $E_\mathrm m$, which is very slightly greater than $E_0$. Saddles become dominant over minima at a higher energy $E_\mathrm{th}$. The 1RSB complexity transitions to a RS description for dominant stationary points at an energy $E_\mathrm{dom}$. The highest energy for which the 1RSB description exists is $E_\mathrm{max}$. The numeric values for all these energies are listed in Table~\ref{tab:energies}. For minima, the complexity does not inherit a 1RSB description until the energy is with in a close vicinity of the ground state. On the other hand, for high-index saddles the complexity becomes described by 1RSB at quite high energies. This suggests that when sampling a landscape at high energies, high index saddles may show a sign of replica symmetry breaking when minima or inherent states do not. Fig.~\ref{fig:2rsb.phases} shows a different detail of the complexity in the vicinity of the ground state, now as functions of the energy difference and stability difference from the ground state. Several of the landmark energies described above are plotted, alongside the boundaries between the `phases.' Though $E_\mathrm{alg}$ looks quite close to the energy at which dominant saddles transition from 1RSB to RS, they differ by roughly $10^{-3}$, as evidenced by the numbers cited above. Likewise, though $\langle E\rangle_1$ looks very close to $E_\mathrm{max}$, where the 1RSB transition line terminates, they too differ. The fact that $E_\mathrm{alg}$ is very slightly below the place where most saddle transition to 1RSB is suggestive; we speculate that an analysis of the typical minima connected to these saddles by downward trajectories will coincide with the algorithmic limit. An analysis of the typical nearby minima or the typical downward trajectories from these saddles at 1RSB is warranted \cite{Ros_2019_Complex, Ros_2021_Dynamical}. Also notable is that $E_\mathrm{alg}$ is at a significantly higher energy than $E_\mathrm{th}$; according to the theory, optimal smooth algorithms in this model stall in a place where minima are exponentially subdominant. \begin{figure} \centering \includegraphics[width=\columnwidth]{figs/24_phases_letter.pdf} \caption{ `Phases' of the complexity for the $2+4$ model in the energy $E$ and stability $\mu$ plane. The region shaded gray shows where the RS solution is correct, while the region shaded red shows that where the FRSB solution is correct. The white region shows where the complexity is zero. } \label{fig:frsb.phases} \end{figure} If the covariance $f$ is chosen to be concave, then one develops FRSB in equilibrium. To this purpose, we choose \begin{equation} f(q)=\frac12\left(q^2+\frac1{16}q^4\right) \end{equation} also studied before in equilibrium \cite{Crisanti_2004_Spherical, Crisanti_2006_Spherical}. Because the ground state is FRSB, for this model \begin{equation} E_0=E_\mathrm{alg}=E_\mathrm{th}=-\int_0^1dq\,\sqrt{f''(q)}=-1.059\,384\,319\ldots \end{equation} In the equilibrium solution, the transition temperature from RS to FRSB is $\beta_\infty=1$, with corresponding average energy $\langle E\rangle_\infty=-0.53125\ldots$. Fig.~\ref{fig:frsb.phases} shows the regions of complexity for the $2+4$ model. Notably, the phase boundary predicted by a perturbative expansion correctly predicts where the finite $k$RSB approximations terminate. Like the 1RSB model in the previous subsection, this phase boundary is oriented such that very few, low energy, minima are described by a FRSB solution, while relatively high energy saddles of high index are also. Again, this suggests that studying the mutual distribution of high-index saddle points might give insight into lower-energy symmetry breaking in more general contexts. We have used our solution for mean-field complexity to explore how hierarchical RSB in equilibrium corresponds to analogous hierarchical structure in the energy landscape. In the examples we studied, a relative minority of energy minima are distributed in a nontrivial way, corresponding to the lowest energy densities. On the other hand, very high-index saddles begin exhibit RSB at much higher energy densities, on the order of the energy densities associated with RSB transitions in equilibrium. More wore is necessary to explore this connection, as well as whether a purely \emph{geometric} explanation can be made for the algorithmic threshold. Applying this method to the most realistic RSB scenario for structural glasses, the so-called 1FRSB which has features of both 1RSB and FRSB, might yield insights about signatures that should be present in the landscape. \paragraph{Acknowledgements} The authors would like to thank Valentina Ros for helpful discussions. \paragraph{Funding information} JK-D and JK are supported by the Simons Foundation Grant No.~454943. \bibliography{frsb_kac-rice} \end{document}