The goal of this post is to introduce the Sherrington-Kirkpatrick model, which is the canonical starting point in Spin-Glass Theory. This model (along with a slightly generalized family) is one of the first models for which the infamous Parisi-Variational Principle was (formally) proven to be true. It is also a model that instigated the introduction of many other concepts via the Replica-Symmetry Breaking ansatzen such as Ultrametricity, the TAP Equations, and the Ghirlanda-Guerra Identities. It is the prototypical model of a Mean-Field Spin Glass and has recently had algorithmic implications, which in conjunction with the growing body of work on the Overlap-Gap Property have shed light on the average-case complexity of a large family of optimization problems1.


Table of Contents

  1. The Sherrington-Kirkpatrick Model
  2. Aizenman-Sims-Starr Scheme
  3. Ruelle Probability Cascades
  4. Footnotes


The Sherrington Kirkpatrick Model

We briefly introduce the Sherrington-Kirkpatrick model as an optimization problem. We will be interested in the almost-surely limit of the optimal value, and we will use an elementary estimate to establish the appropriate normalization in searching for this limit value. The covariance of the optimization problem under consideration will be computed and shown to be a function of the overlap between two solutions - This is a fundamental and critical observation that will show up many times as a quantity of interest in the proof of the Parisi Variational Principle. We will introduce the infamous Guerra-Tonnineli interpolation and use it to demonstrate the existence of (but not compute exactly) the “smoothed” verison of the limit we are interested in on average. Using the interpolation again in conjunction with some convexity arguments, we will then establish a gaussian concentration inequality which will imply that the limit exists almost-surely.

An Average-Case Optimization Problem

Given \(n^2\) i.i.d. standard normal (\(\mathcal{N}(0, 1)\)) variables \(\{J_{ij}\}_{i, j \in [n]}\), we are interested in the optimal value of the following optimization problem over the hypercube,

\[\begin{equation} \max_{\sigma \in \{\pm 1\}^n} \frac{1}{\sqrt{n}}\sum_{i, j=1}^n J_{i,j}\sigma_i\sigma_j\, . \end{equation}\]

The problem above can be seen as asking for the \(\mathsf{MAX}\)-\(\mathsf{CUT}\) of a complete graph with i.i.d. \(\mathcal{N}(0,1)\) weights. As mentioned, we will be interested in the typical optimal value of the above problem as \(n\) gets large. The normalized quadratic form above will be expressed as \(H_n(\sigma)\) which, in physics language, translates to asking for the value of the hamiltonian \(H_n\) under the configuration of spins \(\sigma \in \{\pm 1\}^n\). Therefore, we will ask the following question instead,

\[\begin{equation} \lim_{n \to \infty} \mathbb{E}\left[\max_{\sigma \in \{\pm 1\}^n} H_n(\sigma) \right]\, . \end{equation}\]

While this problem seems well defined, we need to nornalize it carefully so that the limit we wish to compute does not diverge.

Covariance and Overlaps

A simple observation (formalized below) will reveal that the fluctuations (variance) of the term above lead to a divergent limit. Therefore, we must normalize it appropariately. To know what normalization is appropriate, we compute the covariance of the underlying gaussian process explicitly,

\[\begin{align} \mathbb{E}[H_n(\sigma^1)H_n(\sigma^2)] &= \frac{1}{n}\mathbb{E}\left[ \sum_{i_1, j_1}J_{i_1, j_1}\sigma^1_{i_1}\sigma^2_{j_1}\sum_{i_2, j_2}J_{i_2, j_2}\sigma^1_{i_2}\sigma^2_{j_2}\right] \\ &= \frac{1}{n}\mathbb{E}\left[\sum_{i_1, j_1}J^2_{i_1, j_1}\sigma^1_{i_1}\sigma^2_{i_1}\sigma^1_{j_1}\sigma^2_{j_1}\right] \\ &= \frac{1}{n}\left(\sum_{i =1}^n \sigma^1_i\sigma^2_i\right)^2 = n\cdot \left(\frac{1}{n}\langle \sigma^1, \sigma^2\rangle\right)^2\, . \end{align}\]

The above calculation reveals that the covariance of the quantity we wish to maximize is a \(O(n)\) scaling of the normalized overlap between two configurations \(\sigma^1\) and \(\sigma^2\). Since the latter quantity is a constant, this tells us that the extra normalization term we wish to add divides the quantity to optimize by \(n\). This will yield the final average quantity whose limit we wish to compute precisely,

\[\begin{equation} \lim_{n \to \infty} \frac{1}{n} \mathbb{E}\left[\max_{\sigma \in \{\pm 1\}^n} H_n(\sigma)\right]\, , \end{equation}\]

and this quantity will be termed the Ground State Energy of the system. When studying such quantities, it is a standard trick in statistical physics to understand this quantity using its smoothed approximation, known as the Free Energy Density of the system. This quantity depends on the smoothing parameter \(\beta\) and is defined as,

\[\begin{equation} F_{n, \beta} := \frac{1}{n}\mathbb{E}\left[\log\left(\sum_{\sigma \in \{ \pm 1\}^n} e^{\beta H_n(\sigma)}\right)\right] \, , \end{equation}\]

where the exponential summation term,

\[\begin{equation} Z_{n, \beta} := \sum_{\sigma \in \{\pm 1\}^n}e^{\beta H_n(\sigma)}\, , \end{equation}\]

is termed the Partition function in Statistical Physics. As we shall see, this softmax trick induces a measure called the Gibbs Measure which will turn out to be very closely related to the Free Energy of the model and will appear as soon as a derivative of the latter is taken with respect to an appropriate parameter in the hamiltonian. Understanding the asymptotic geometric structure of the gibbs, therefore, will be of paramount importance in formally proving the Parisi Variational Principle.

The smoothed free energy can be related to the ground state energy in the regime that the smoothing parameter \(\beta \to \infty\). This is made precise in the following,

(Proposition-1): The following holds for all \(\beta > 0\),

\[\begin{equation} \lim_{n \to \infty} \frac{1}{n} \mathbb{E}\left[\max_{\sigma \in \{\pm 1\}^n} H_n(\sigma)\right] \leq \lim_{n \to \infty} \frac{1}{\beta n}\mathbb{E}\left[\log\left(\sum_{\sigma \in \{\pm 1\}^n}e^{\beta H_n(\sigma)}\right)\right] \leq \lim_{n \to \infty} \frac{1}{n} \mathbb{E}\left[\max_{\sigma \in \{\pm 1\}^n} H_n(\sigma)\right] + \frac{\log(2)}{\beta} \, . \end{equation}\]

Note that the above immediately implies (via an application of Holder’s inequality) that,

\[\begin{equation} \lim_{n \to \infty} \frac{1}{n} \mathbb{E}\left[\max_{\sigma \in \{\pm 1\}^n} H_n(\sigma)\right] = \lim_{\beta \to \infty}\frac{1}{\beta}\left(\lim_{n \to \infty} F_{n, \beta}\right)\, , \end{equation}\]

if we assume that the \(n \to \infty\) limit (also called the thermodynamic limit) of the free energy density exists.

Guerra-Tonnineli Interpolation

We now introduce a smooth interpolation between three independent instances of the SK model, and in conjunction with the lemmas of Fekete and Stein, use it show that the thermodynamic limit of the free energy density exists.

Before introducing the interpolation formally, we briefly state Fekete’s and Stein’s lemmata. The proofs for these are elementary and require no more than basic algebraic manipulations and integration by parts, and are omitted in this blog post2.

(Stein's Lemma): Given a differentiable function \(f\) that doesn’t grow too fast, and a jointly-gaussian process \(\{g(\sigma)\}_{\sigma \in \Sigma}\), the following holds,

\[\begin{equation} \mathbb{E}[g_{\sigma}f(g)] = \sum_{\sigma' \in \Sigma}\mathbb{E}\left[g_{\sigma}g_{\sigma'} \right]\mathbb{E}\left[\partial_{\sigma'}f(g)\right]\, . \end{equation}\]

(Fekete's Lemma): If a sequence \(\{x_n\}_{n = 1}^{\infty}\) is superadditive (\(x_n + x_m \leq x_{n + m}\, ,\, \forall n, m \geq 1\)), then,

\[\begin{equation} \lim_{n \to \infty} \frac{x_n}{n} = \sup_{n \geq 1}\frac{x_n}{n}\, . \end{equation}\]

We now state the interpolation, and give some intuition about its structure before proving the main lemma which asserts the existence of the limit for the free energy density as \(n \to \infty\).

(Guerra-Tonnineli Interpolation): Given three independent instances of the SK model on \(n, m\) and \(n + m\) particles, the interpolation is defined \(\forall t \in [0, 1]\) as follows,

\[\begin{equation} H^t(\sigma) = \sqrt{t} H_{n + m}(\rho\cdot\tau) + \sqrt{1-t}\left(H_n(\rho) + H_m(\tau)\right)\, . \end{equation}\]

Notice that the interpolation above is equivalent to two independent copies of the SK model of size \(n\) and \(m\) at \(t = 0\), and becomes a single copy of the SK model of size \(n + m\) at \(t=1\). The square-roots are introduced to keep the variance of a single gaussian interaction in the interpolated gaussians within the graphs of size \(n\) and \(m\) to \(1\), while slowly increasing the variance of the gaussian interactions between the two graphs from \(0\) to \(1\). As we shall see, the interpolation helps prove that the free energy is superadditive and an application of Fekete’s lemma immediately implies the existence of the limit.

(Lemma-1): The free energy exists in the thermodynamic limit: \(\lim_{n \to \infty} F_{n, \beta}\) exists.
Proof: We will analyze the free energy density \(\phi(t)\) of the interpolated hamiltonian \(H^t\) at every \(t \in [0, 1]\). Note that since \(J_{i, j}\) are continuously distributed and the expectation is a convex combination of continuous variables, \(\phi(t)\) is continuous. The change of free energy density as a function of \(t\) is then given as,

\[\begin{align} \partial_t\phi(t) &= \frac{1}{n + m}\mathbb{E}\left[ \frac{1}{Z_t}\partial_t(Z_t)\right] = \frac{1}{n + m}\mathbb{E}\left[ \frac{1}{Z_t}\left(\sum_{\sigma \in \{\pm 1\}^n}\partial_te^{ H^t(\sigma)}\right)\right] \\ &= \frac{1}{n + m}\mathbb{E}\left[ \langle \partial_t H^t(\sigma)\rangle_t\right]\, ,\end{align}\]

where \(\langle . \rangle_t\) denotes the average with respect to the Gibbs measure at \(t\). The above relationship states that the free energy density changes proportional to the average rate of change of the energy of the interpolated hamiltonian. The above expression is evaluated by applying a wonderful lemma that allows us to rewrite the expected value of some jointly gaussian vector \(\{x_{\sigma}\}\) in terms of a term that subtracts the “covariance” between \(\{x_{\sigma}\}\) and another gaussian vector \(\{y_{\sigma}\}\) from the “overlap” terms3.

(Gaussian Covariance for Gibbs Average [Lemma 1.1, Pa14]): Given two jointly gaussian vectors \(\{x_{\sigma}\}\) and \(\{y_{\sigma}\}\), the following can be said about the iterated average of \(x_{\sigma}\) (where the average is with respect to the Gibbs for \(\{y_{\sigma}\}\)),

\[\begin{equation} \mathbb{E}[\langle x_\sigma \rangle] = \mathbb{E}[\langle \mathbb{E}[x_{\sigma_1}y_{\sigma_1}]\rangle - \langle \mathbb{E}[x_{\sigma_1}y_{\sigma_2}]\rangle]\end{equation}\, .\]

The lemma above allows us to re-write the average rate of change in the ground state energy of the interpolated hamiltonian in terms of a covariance term with respect to the hamiltonian itself, which can be expanded and evaluated in terms of the overlaps. Setting \(x_{\sigma} = \partial_t H^t(\sigma)\) and \(y_{\sigma} = H^t(\sigma)\), we have

\[\begin{align} \frac{1}{n + m}\mathbb{E}\left[ \langle \partial_t H^t(\sigma)\rangle_t\right] &= \frac{1}{n + m}\mathbb{E}\left[\langle\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_1) \right]\rangle_t - \langle\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_2)\right]\rangle_t\right] \end{align}\, .\]

The terms inside the expectations can easily be evaluated by taking the derivatives and multiplying to be,

\[\begin{align} &\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_1) \right] = \mathbb{E}\left[ \frac{1}{2}(H_{n + m}(\sigma_1))^2 - (H_n(\rho_1) + H_m(\tau_1))^2\right] \\ &= (n + m)\left(\frac{\langle\sigma_1, \sigma_1\rangle}{n + m}\right)^2 - n\left(\frac{\langle \rho_1, \rho_1 \rangle}{n}\right)^2 - m\left(\frac{\langle \tau_1, \tau_1 \rangle^2}{m}\right) \\ &= 0\, ,\end{align}\]

where we used the fact that \(H_n\) and \(H_m\) are independent to zero out the covariance term in-between them in conjunction with the fact that the covariance of the underlying gaussian process is the square of its normalized overlaps.

A similar calculation immediately reveals that,

\[\begin{align} &\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_2)\right] = \mathbb{E}\left[\frac{1}{2}(H_{n + m}(\sigma_1)H_{n + m}(\sigma_2)) - H_n(\rho_1)H_n(\rho_2) - H_m(\tau_1)H_m(\tau_2)\right] \\ &= \frac{1}{2}(n + m)\left(\frac{\langle \sigma_1, \sigma_2 \rangle}{n + m}\right)^2 - n\left(\frac{\langle \rho_1, \rho_2 \rangle}{n}\right)^2 - m\left(\frac{\langle\tau_1, \tau_2 \rangle}{m}\right)^2 \\ &= \frac{1}{2}\frac{n}{n+m}\left(\frac{n}{n + m} - 1\right)\left(\frac{\langle \rho_1, \rho_2 \rangle}{n}\right)^2 + \left(\frac{m}{n + m} - 1\right)\left(\frac{\langle \sigma_1, \sigma_2 \rangle}{m}\right)^2 + \frac{2mn}{m + n}\left(\frac{\langle\rho_1,\rho_2\rangle\langle\tau_1,\tau_2\rangle}{mn}\right)^2 \end{align}\]

Note that the term above is negative since it is equivalent to

\[\begin{align} \frac{1}{m + n}\left(2AB - mA - nB\right)\, ,\forall n, m \geq 1\, , \end{align}\]

where \(A = n\left(\frac{\langle \rho_1, \rho_2\rangle}{n}\right)^2\) and \(B = m\left(\frac{\langle \tau_1, \tau_2\rangle}{m}\right)^2\), and to assert negativity we used the facts that \(|A|, |B| \leq 1\) and \(\begin{equation} \langle \sigma_1, \sigma_2 \rangle = \frac{n}{m + n}\left(\frac{\langle\rho_1, \rho_2\rangle}{n}\right) + \frac{m}{m + n}\left(\frac{\langle \tau_1, \tau_2 \rangle}{m}\right)\, . \end{equation}\)

This immediately implies that,

\[\begin{align} \partial_t\phi(t) &= \frac{1}{n + m}\mathbb{E}\left[\langle\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_1) \right]\rangle_t - \langle\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_2)\right]\rangle_t\right] \\ &= \frac{-1}{m+n}\mathbb{E}\left[\langle\mathbb{E}\left[\partial_tH^t(\sigma_1)H^t(\sigma_2)\right]\rangle_t\right] = -\mathbb{E}\left[\langle\textsf{g}(\text{overlaps}^2)\rangle_t\right] \geq 0\, , \end{align}\]

since the expectations of non-negative random variables are non-negative. Additionally, these are convex combinations of the squares of the overlap, which are convex functions themselves. Therefore, \(\partial_t\phi(t)\) is a convex function. Then, the observations that,

\[\begin{align} &\phi(0) = \frac{n}{m + n}F_n + \frac{m}{m + n}F_m \, , \\ &\phi(1) = F_{n + m} \, , \end{align}\]

yield that the free energy is superadditive (since \(\partial_t\phi(t) \geq 0\) and convexity implies \(\phi(0) \leq \phi(1)\)). This shows that the limit of the free energy density is well-defined in the thermodynamic limit with an application of Fekete’s Lemma.

Gaussian Concentration

The goal of this section is to “boost” the previous lemma showing that the free energy density of the SK model is well-defined on average to an almost-surely statement. In order to do that it is crucial to show that there is concentration of the log-partition function (under the gaussians). To do this, we will use a gaussian concentration inequality that is proved using a gaussian interpolation technique (which is very similar to the Guerra-Tonnineli interpolation) in conjunction with some elementary convexity properties3.

(Gaussian Concentration Inequality): Given a \(b\)-lipschitz function \(F:\mathbb{R}^n \to \mathbb{R}\), and a jointly gaussian process \(\{g_i\}_{i=1}^n\) with bounded covariance \(C\), the following holds \(\forall \epsilon > 0\)

\[\begin{equation} \Pr_g\left[\left|F(g_1,\dots,g_n) - \mathbb{E}_g\left[F(g_1,\dots,g_n)\right]\right| \geq \epsilon\right] \leq 2e^{-\frac{\epsilon^2}{4b^2 C}}\, . \end{equation}\]

Note that choosing \(F(\{g_{\sigma}\}) = \log\left(\sum_{\sigma \in \{\pm 1\}^n}e^{g_{\sigma}}\right) = \log(Z_n)\) with \(C = \mathbb{E}[H_n(\sigma)^2] = 1\) and \(b = \sqrt{C} = 1\), immediately yields concentration for the partition function/free energy density as,

\[\begin{equation} \Pr_g\left[\left|\frac{1}{n}\log\left(Z_n\right) - \frac{1}{n}\mathbb{E}\left[\log(Z_n)\right]\right| \geq \epsilon \right] \leq 2e^{-\frac{\epsilon^2 n}{4}}\, .\end{equation}\]

With the result above, we can argue that the limit of the free energy density exists almost-surely for large enough system sizes.

Aizenman-Sims-Starr Scheme

We now introduce a scheme that will derive an expression for the change in the free energy of the system when a “cavity” is created, or equivalently, we decrease the size of the input instance by removing one vertex. Rewriting the free energy as a telescoping sum over the cavities then allows one to get an expression for the free energy - This term is known as the Aizenman-Sims-Starr (ASS) functional. Unfortunately, it is not possible to show that the limit exists for the ASS functional, but we can use it to lower bound the free energy of the SK model by showing that its \(\lim\inf\) exists. The close relationship of the Gibbs measure with the free energy is made clear in two ways:

  1. The ASS functional depends on the Gibbs measure, and a telescoping sum shows that the free energy is a Gibbs-averaged quantity.
  2. We will also state a result that reveals a close relationship between the structure of the overlap distribution induced by the Gibbs measure and the ASS functional - As will become clearer later, this is a deep result that hints at a tight connection between the two seemingly different approaches of the Replica Method and Cavity Method used extensively in Statistical Physics.

The ASS functional

We begin by introducing the quantity that measures the difference in the free energy on instances where the size differs by one,

\[\begin{equation} A_j = \mathbb{E}[\log(Z_{j+1})] - \mathbb{E}[\log(Z_j)]\, ,\, \forall j \in \{0,\dots,n-1\}\, . \end{equation}\]

This immediately yields the following observation,

\[\begin{equation} F_{n, \beta} = \frac{1}{n}\mathbb{E}[\log(Z_n)] = \frac{1}{n}\sum_{i=0}^{n-1}A_i\, .\end{equation}\]

To compute \(A_n\) we begin by computing \(H_{n + 1}\) and try to write it as \(H_n\) plus some other term. This other term will then roughly correspond to the increase in the free energy from the additional of the cavity vertex.

\[\begin{equation} H_{n+1}(\sigma.\tau) = H'_{n}(\sigma) + \tau\cdot y(\sigma) + \frac{1}{\sqrt{n+1}}g_{n+1, n+1}\end{equation} \, ,\]

where,

\[\begin{align} H'_{n}(\sigma) &= \frac{1}{\sqrt{n+1}}\sum_{i, j = 1}^n g_{i, j}\sigma_i\sigma_j\, , \\ y(\sigma) &= \frac{1}{\sqrt{n+1}}\sum_{i=1}^n(g_{i, n+1} + g_{n+1, i})\sigma_i \, ,\\ &\tau \in \{\pm 1\} \, . \end{align}\]

Note that, upto a different normalization factor, the expression above for \(H_{n+1}(\sigma)\) establishes the desired recursive formulation. In order to write directly in terms of the hamiltonian \(H_n(\sigma)\) (as opposed to \(H'_n(\sigma)\)) we use two observations:

  • The sum of two independent gaussians is another gaussian.
  • The covariance of a centered gaussian process can characterize it.

To begin, note that, \(\begin{equation} \mathbb{E}[H'_n(\sigma_1)H'_n(\sigma_2)] = \frac{n^2}{n + 1}\left(\frac{\langle \sigma_1, \sigma_2 \rangle}{n}\right)^2\, . \end{equation}\)

We’d like it to be the case that when we add another centered gaussian process \(\{x_\sigma\}\) that is normalized appropriately, this covariance will be become exactly \(n\). So, we need a process with covariance \(\frac{n}{n + 1}\) times the overlap on \(n\) vertices that is independent of the gaussians in $H’_n$. Note that such a process can be given by,

\[\begin{equation} x(\sigma) = \frac{1}{\sqrt{n(n + 1)}}\sum_{i, j =1}^ng'_{i, j}\sigma_i\sigma_j\, ,\end{equation}\]

where \(\{g'_{i,j}\}\) are independent of the gaussian interactions in \(H'_n(\sigma)\). Therefore, in distribution,

\[\begin{equation} H_n(\sigma) \overset{d}{=} H'_n(\sigma) + x(\sigma)\, , \end{equation}\]

which yields the final following cavity equation for the Aizenman-Sims-Starr functional,

\[\begin{align} A_n &= \mathbb{E}[\log(Z_{n+1})] - \mathbb{E}[\log(Z_n)] \\ &= \mathbb{E}\left[\log\left(\sum_{\sigma, \tau}e^{\beta (H'_n(\sigma) + \tau\cdot y(\sigma) + (n + 1)^{-1/2}g_{n+1, n+1})}\right)\right] - \mathbb{E}\left[\log\left(\sum_{\sigma}e^{\beta(H'_n(\sigma) + x(\sigma))}\right)\right] \\ &= \mathbb{E}\left[\log\left(\left\langle \sum_{\tau}e^{\beta \tau\cdot y(\sigma)}\right\rangle'\right)\right] - \mathbb{E}\left[\log\left(\langle e^{\beta x(\sigma)}\rangle'\right)\right] \\ &= \mathbb{E}\left[\log\left(\left\langle 2\cosh(\beta y(\sigma)) \right\rangle'\right)\right] - \mathbb{E}\left[\log\left(\langle e^{\beta x(\sigma)}\rangle'\right)\right]\, ,\end{align}\]

where we used the fact that the self-interacting gaussian term averages to \(0\) as the gaussians in consideration are centered, and \(\langle . \rangle'\) denotes a Gibbs measure with respect to the re-scaled hamiltonian \(H'_n(\sigma)\) on \(n\) vertices. A few comments about the functional \(A_n\) are in order:

  • The functional depends on the average with respect to the Gibbs measure \(\langle . \rangle'\) defined on the (slightly rescaled) SK hamiltonian on \(n\) vertices.
  • The quantity with respect to which the gibbs measure is made has gaussians that are completely independent of the gaussians in \(x(\sigma)\) and \(y(\sigma)\).
  • The gaussian processes \(x(\sigma)\) and \(y(\sigma)\) are defined on \(n\) vertices.
  • The decomposition works because a common quantity on \(n\) vertices (\(H'_n\)) is found, which allows us to write the free energy on \(n\) and \(n + 1\) vertices as the shared process plus some independent gaussian processes.

The last point is critical: Since we can decouple the hamiltonians on both vertices as a shared gaussian process plus two independent processes, we can actually average (with respect to the Gibbs measure) over the contribution that comes from the cavity vertex, keeping the same iterated average over the Gibbs from both free energy terms that contribute to \(A_n\).

Invariance Symmetries

As it turns out, in a statement that is not too hard to prove but remarkably insightful, the rotational-invariance of the distribution of the so-called overlap matrix can be related in a very precise way to the ASS functional defined above. In doing this, a deep connection between the seemingly unrelated approaches of the Replica method and the Cavity method is uncovered - Namely, that one can use information from finite entries of the overlap matrix to arbitrarily well approximate the ASS functional which is itself a proxy (for at least a lower bound) of the free energy density in the asymptotic limit. This, as it turns out, is not a coincidence, and becomes a critical fact in Panchenko’s approach to proving the lower bound:

\[\begin{equation} \lim_{n \to \infty} F_{n, \beta} = \lim_{n \to \infty} A_n \geq \liminf_{n \to \infty} A_n \geq \inf_{\mu}P_\beta(\mu)\, , \end{equation}\]

which is the hard direction in the proof of the Parisi Variational Principle.

Ruelle Probability Cascades

The Ruelle Probability Cascades are a class of random measures on a separable Hilbert Space. To motivate this structure, it is critical to introduce a statement of the Parisi Variational Principle (PVP). There are many equivalent statements of the principle, but we will focus on the one that helps see the parallel with the construction of the Rulle Probability Cascades in a relatively transparent manner. The PVP gives a variational optimizaton problem, the solution to which yields the precise asymptotic value of the free-energy density of the SK model at any temperature \(\beta\) - In this sense (among others) the PVP is stronger than the problem we are interested in since we merely need the \(\beta \to \infty\) limit, as that specifically that corresponds to the \(\mathsf{MAX}\)-\(\mathsf{CUT}\) problem over the complete graph with i.i.d. standard gaussian weights. With this definition in mind, we will introduce the construction of the Ruelle Probability Cascades. In a future post, we will then introduce the Guerra RSB bound to show that one can upper bound the limit of \(\frac{1}{n}\mathbb{E}[\max_{\sigma}H_n]\) as \(n \to \infty\) with the Parisi Variational Principle4. In doing this, the Ruelle Probability Cascades (RPCs) will play a critical role since they will provide an alternative representation of the PVP which will look extremely similar to the ASS functional.

The Parisi Variational Principle

We specify the Parisi Variational Principle after fixing two sequences of parameters that generate the “overlaps” (a sequence \(\{q_i\}^r_{i=0}\)) in the support of a distribution \(\xi\) along with the cumulative density associated with them given by (through an abuse of notation) a sequence \(\{\xi_i\}_{i=-1}^r\). Note that the overlaps are between 0 and 1, so this information can be uniquely identified by a probability distribution \(\mathcal{D}\) over the interval \([0, 1]\). So, we formally specify this distribution as, \(\begin{equation} 0 = \xi_{-1} < \xi_0 < \dots < \xi_{r-1} < \xi_r = 1\, , \end{equation}\)
and \(\begin{equation} 0 = q_0 < q_1 < \dots < q_{r-1} < q_r = 1\, \end{equation}\) with the constraint that \(\xi(\{q_a\}) = \xi_{a} - \xi_{a-1}\) enforcing that we are thinking of \(\xi\) as a CDF.
We now by a sequence of random variables where,

  • A base random variable is defined by a “smoothed” absolute value given to the increase in overlap between two parameters \(q_a\) and \(q_{a+1}\) weighted by some standard gaussian variable \(z_{a}\).
  • The rest of the variables are defined recurively as the average (over a bunch of independent standard gaussians) of a \(\xi\)-weighted sum of prior random variables.

It is not easy to motivate why one would define such a sequence of random variables without some knowledge of the Replica Method and the introduction of the Replica-Symmetry Breaking ansatz that ensues. In a nutshell: The goal of the Replica Method is to calculate the free energy density by evaluating a certain modified form of a moment of the partition function. In doing so, some form of “weights” on overlaps between configurations of independent copies of the model are assumed. This generates an overlap matrix, on which a very specific structure is imposed through the RSB ansatzen - This specific struture is imposed to make some smoothed saddle-point equations tractable, and these equations under the RSB ansatzen in a certain limiting sense give rise to the family of parameters that very closely resemble the Parisi Sequence defined above (and more rigorously below).

(Parisi Sequence): Given i.i.d. standard gaussian random variables \(\{z_a\}_{a=1}^r\), the Parisi Sequence is a sequence of random variables \(\{X^{\xi}_a\}_{a=1}^r\) defined recursively as,

\[\begin{align} X^{\xi}_r &= \log\left(2\cosh\left(\sum_{1 \leq a \leq r}\sqrt{2}\beta(q_a - q_{a-1})^{1/2}z_a \right)\right)\, ,\\ X^{\xi}_b &= \frac{1}{\xi_b}\log\left(\mathbb{E}_{z_{b+1}}\left[\exp(\xi_{b+1}X^{\xi}_{b+1})\right]\right)\, ,\, 0 \leq b \leq r-1\, .\end{align}\]

The Parisi-Variational Principle is given by taking the last (or first, depending on how one views the sequence) term of the Parisi Sequence \(X^{\xi}_0\) minus the temperature normalized variance of the overlap distribution. The Parisi Sequence is obtained as a solution of the so-called Parisi PDEs using the infamous Hopf-Cole Transformation.

(Parisi Variational Principle): The Parisi Variational Principle is an optimization problem over the space of distributions with support \([0, 1]\) and is given for all \(\beta > 0\) as,

\[\begin{equation} \lim_{n \to \infty} F_{n, \beta} = \inf_{\xi \in \mathcal{D}[0, 1]}\left(X^{\xi}_0 - \beta^2\int_{0}^1t\xi(t)dt\right)\end{equation}\, ,\]

where \(\xi(t)\) is being interpreted as the CDf of the distribution over the overlaps \(\{q_a\}\).

The Parisi Variational Principle is claiming that at any finite temperature, the free energy denesity of the SK model in the thermodynamic limit can be expressed as a variational optimization problem over the space of distributions with support \([0, 1]\) which minimize a so-called “free entropy” term \(X^{\xi}_0\) obtained by solving a PDE and the “variance” of the overlaps induced by the solutions.

The RPC Construction

To that extent, we first define an indexing procedure into an \(\infty\)-ary tree where each of the vertices are indexed by a \(k\)-tuple if they are at depth \(k\) in the tree. A path \(\pi(a)\) from the root of the tree to the vertex \(a\) at depth \(k\) can be uniquely identified as,

\[\begin{equation} \pi(a) = \{a_1, (a_1, a_2), \dots, (a_1,\dots,a_{k-1}), (a_1,\dots,a_k)\}\, ,\, a_i \in \mathbb{N}\, , \end{equation}\]

where the root \(\phi\) of the tree is implicit in the sequence. This allows us to associate a unique path to every vertex in the RPC tree.

The RPC tree is a formal realization of the so-called ultrametric tree envisioned by Parisi with probabilistic information attached to every node. This probabilistic information is given by a Poisson-Dirichlet process independently instantiated at each inner node of the tree with mean measure paramterized by the Parsi order parameter. The “cascade” of Poisson-Dirichlet processes associated with every inner vertex then induce a natural random measure on the leaves of the tree which approaches the Gibbs measure in the asymptotic limit.

We first describe the structure of the RPC tree and how to construct the random measure on its leaves.

(RPC Tree Inner Vertices): Given a Parisi order parameter

\[\begin{equation} \zeta = (\{\xi\}_{i=-1}^r, \{q_i\}_{i=0}^r)\, , \end{equation}\]

we define the position of the inner vertices

\[\begin{equation} \{v_{\alpha_1,\dots,\alpha_k}\}_{\alpha_i \in \mathbb{N}^i}\, ,\, \forall k \in [r-1]\, , \end{equation}\]

of the RPC tree as,

\[\begin{equation} v_{\alpha_1,\dots,\alpha_k} = \sum_{i=0}^{k}\bigg(\sqrt{q_{i+1} - q_i}\bigg)e_{\alpha_i}\, , \end{equation}\]

where \(\{e_{\alpha_i}\}\) is a set of orthonormal basis over the underlying Hilbert Space.

We now define the probabilistic information associated with the inner vertices of the RPC tree. To do this, we first introduce the notion of Poisson-Dirichlet processes.

(Poisson-Dirichlet Process): A Poisson-Dirichlet Point Process with parameter \(\eta\) is defined as the unique stochastic process \(\{X_l\}_l\) such that \(\forall A \subseteq \mathbb{R}^n\),

\[\begin{equation} \mathbb{P}(X_l \in A) \overset{d}{=} \textsf{Poisson}(\mu(A))\,, \end{equation}\]

where,

\[\begin{equation} \mu(A) = \int_{x \in A}\eta x^{-1-\eta}dx\, . \end{equation}\]

In order to have a cascading process, we will associate an independent Poisson-Dirichlet point process \(\Pi_{v_{\alpha_1,\dots,\alpha_k}}\) to every inner vertex with mean measure \(\mu(dx) = \xi_{k}x^{-1-\xi_k}dx\). In fact, to define the indices of the children of every inner vertex (including the root) we will take a decreasing sequence \(\{u_{\alpha_1,\dots,\alpha_k,n}\}_{n \in \mathbb{N}}\) and use that to index the children vertices of \(v_{\alpha_1,\dots,\alpha_k}\). As it turns out, this construction allows us to associate a “weight” \(w_{\alpha_1,\dots,\alpha_k}\) to every vertex in the tree (including the leaves) and this quantity is almost surely finite, thereby allowing one to define the required random measure on the leaves of the tree at level \(r\).

(Weights to Vertices): To every inner vertex \(v_{\alpha_1,\dots,\alpha_k}\) in the RPC tree, a weight

\[\begin{equation} w_{\alpha_1,\dots,\alpha_k} := \prod_{\beta \in \pi(\alpha)} u_{\beta}\, , \end{equation}\]

is associated.

Since the sum of these weights along the leaves is finite (almost surely), this allows us to define the random measure in the support of the RPC tree \(\textsf{RPC}(\zeta)\) simply as,

\[\begin{equation} w_{v} := \frac{\prod_{\beta \in \pi(v)}}{\sum_{\alpha \in \mathbb{N}^r} w_{\alpha}}\, , \end{equation}\]

where the sum in the denominator is a normalizing term to make sure that \(w_v\) is a measure.

With the definitions above, we point out one elementary property about RPC trees that will be very useful when moving forward. This property asserts that the “overlap” between two vertices in the tree is given by their lowest common ancestor.

(RPC Overlaps): The overlap between any two vertices \(v_{\alpha_1,\dots,\alpha_k}\) and \(w_{\beta_1,\dots,v_l}\) is given as

\[\begin{equation} \langle v_{\alpha_1,\dots,\alpha_k}, w_{\beta_1,\dots,\beta_l} \rangle = q_i\, , \end{equation}\]
where $$i = \pi(\alpha_1,\dots,\alpha_k) \cap \pi(\beta_1,\dots,\beta_l) $$.

This is an elementary property and the proof follows by substituting the definition of the location of the points. However, it already shows that the geometry of the RPC construction is such that it reflects the hierarchical organization of overlaps of points (leaves) on which \(1 - o_n(1)\) fraction of the Gibbs measure concentrates. This is in precise agreement with the Parisi ultrametric tree. The RPC tree also has the added bonus of reducing the process of generating the asymptotic Gibbs measure to an object that can be studied by tools from the theory of Poisson-Dirichlet point processes. As it turns out, the RPCs are a fundamental structure in the theory of Mean-Field Spin Glasses as they satisfy some critical properties and provide an alternative structure on which the Parisi variational principle can be recast5 - This ends up being essential to the proof of the upper bound as well as the lower bound.

FOOTNOTES

  1. There are many references to this body of work which will be given in due course. Nonetheless, the following two surveys are a nice start: [B05], [G21]

  2. Stein’s Lemma can be proved by a simple integration-by-parts argument for every coordinate \(i\) in conjunction with Fubini’s theorem and the chain-rule. The lemma itself simply asserts that the correlation between a gaussian variable and some function of it is equivalent to a sum of scalings (by the correlations) of the average rate of change of the function. Likewise, Fekete’s Lemma boils down to algebraic manipulation of the sequence, where we take the limit infimum of \(x_n/n\) and compare it to limit infimum of some \(x_m\) where \(m\) is chosen to be the supremum as a divisor with a remainder term. The lemma itself merely asserts that for an appropriately growing sequence, the empirical average in the limit simply picks out the largest contributing term. 

  3. In this upcoming post, various results from the so-called “Gaussian Toolbox” will be stated and briefly proved. This is a very useful set of techniques to have command over in order to prove properties about mean-field spin glasses, and relate them to the behavior of random instances of sparse CSPs.  2

  4. In this upcoming post, we introduce the Guerra-RSB bound and prove that it can be used to show that the Parisi Variaional Principle, represented as an appropriately parameterized RPC, can be used to upper bound the free energy density of the SK model. We will also introduce the famed Ghirlanda-Guerra identities. 

  5. In this upcoming post, we will introduce some fundamental properties that the RPC tree satisfies, and then derive an alternative representation of the Parisi Variational Principle which can be stated purely as an optimization of a functional whose random measure is supported over the leaves of a RPC tree.