Disorder chaos and multiple valleys in spin glasses
Abstract.
We prove that the SherringtonKirkpatrick model of spin glasses is chaotic under small perturbations of the couplings at any temperature in the absence of an external field. The result is proved for two kinds of perturbations: (a) distorting the couplings via OrnsteinUhlenbeck flows, and (b) replacing a small fraction of the couplings by independent copies. We further prove that the SK model exhibits multiple valleys in its energy landscape, in the weak sense that there are many states with nearminimal energy that are mutually nearly orthogonal. We show that the variance of the free energy of the SK model is unusually small at any temperature. (By ‘unusually small’ we mean that it is much smaller than the number of sites; in other words, it beats the classical Gaussian concentration inequality, a phenomenon that we call ‘superconcentration’.) We prove that the bond overlap in the EdwardsAnderson model of spin glasses is not chaotic under perturbations of the couplings, even large perturbations. Lastly, we obtain sharp lower bounds on the variance of the free energy in the EA model on any bounded degree graph, generalizing a result of Wehr and Aizenman and establishing the absence of superconcentration in this class of models. Our techniques apply for the spin models and the Random Field Ising Model as well, although we do not work out the details in these cases.
Key words and phrases:
SherringtonKirkpatrick model, EdwardsAnderson model, spin glass, chaos, disorder, multiple valleys, concentration of measure, low temperature phase, Gaussian field2000 Mathematics Subject Classification:
60K35, 60G15, 82B44, 60G60, 60G70Contents
 1 Introduction
 2 Proof sketches

3 General results about Gaussian fields and proofs
 3.1 Chaos in Gaussian fields
 3.2 Proof of Theorem 1.4
 3.3 Multiple valleys in Gaussian fields
 3.4 Proofs of Theorem 1.1 and Corollary 1.2
 3.5 Superconcentration in Gaussian fields
 3.6 Proof of Theorem 1.5
 3.7 Chaos implies superconcentration
 3.8 A formula for the variance of Gaussian functionals
 3.9 Proof of Theorem 1.8
 3.10 Proof of Theorem 1.6
 3.11 Proof of Theorem 1.7
 3.12 Chaos under discrete perturbation
 3.13 Proof of Theorem 1.3
 3.14 Sharpness of Theorem 3.1 for the REM
1. Introduction
Spin glasses are magnetic materials with strange properties that distinguish them from ordinary ferromagnets. In statistical physics, the study of spin glasses originated with the works of Edwards and Anderson [11] and Sherrington and Kirkpatrick [33] in 1975. In the following decade, the theoretical study of spin glasses led to the invention of deep and powerful new methods in physics, most notably Parisi’s broken replica method. We refer to [26] for a survey of the physics literature.
However, these physical breakthroughs were far beyond the reach of rigorous proof at the time, and much of it remains so till date. The rigorous analysis of the SherringtonKirkpatrick model began with the works of Aizenman, Lebowitz and Ruelle [1] and Fröhlich and Zegarliński [15] in the late eighties; the field remained stagnant for a while, interspersed with a few nice papers occasionally (e.g. [8], [32]). The deepest mysteries of the broken replica analysis of the SK model remained mathematically intractable for many more years until the pathbreaking contributions of Guerra, Toninelli, Talagrand, Panchenko and others in the last ten years (see e.g. [2], [19], [18], [30], [17], [34], [35]). Arguably the most notable achievement in this period was Talagrand’s proof of the Parisi formula [35].
However, in spite of all this remarkable progress, our understanding of these complicated mathematical objects is still shrouded in mystery, and many conjectures remain unresolved. In this article we attempt to give a mathematical foundation to some aspects of spin glasses that have been wellknown in the physics community for a long time but never before penetrated by rigorous mathematics. Let us now embark on a description of our main results. Further references and connections with the literature will be given at the appropriate places along the way.
1.1. Weak multiple valleys in the SK model
Consider the following simplelooking probabilistic question: Suppose are i.i.d. standard Gaussian random variables, and we define, for each , the quantity
(1) 
Then is it true that with high probability, there is a large subset of such that
(2) 
and any two distinct elements of are nearly orthogonal, in the sense that
(3) 
(In the spin glass literature, the quantity is called the ‘overlap’ between the ‘configurations’ and .) To realize the nontriviality of the question, consider a slightly different Gaussian field on , defined as
where are i.i.d. standard Gaussian random variables. Then clearly, is maximized at , where . Note that for any ,
It is not difficult to argue from here that if is another configuration that is nearmaximal for , then must agree with at nearly all coordinates. Thus, the field does not satisfy the ‘multiple peaks picture’ that we are investigating about . This is true in spite of the fact that and are approximately independent for almost all pairs .
We have the following result about the existence of multiple peaks in the field . It says that with high probability, there is a large collection of configurations satisfying (2) and (3), that is, for any two distinct , and for each .
Theorem 1.1.
Let be the field defined in (1), and define the overlap between configurations by the formula (3). Let
Then there are constants , , , and such that with probability at least , there is a set satisfying

,

for all , , and

for all .
Quantitatively, we can take , , and , where is an absolute constant. However these are not necessarily the best choices.
Let us now discuss the implication of this result in spin glass theory. The SherringtonKirkpatrick model of spin glasses, introduced in [33], is defined through the Hamiltonian (i.e. energy function)
(4) 
The SK model at inverse temperature defines a probability measure on through the formula
(5) 
where is the normalizing constant. The measure is called the Gibbs measure.
According to the folklore in the statistical physics community, the energy landscape of the SK model has ‘multiple valleys’. Although no precise formulation is available, one way to view this is that there are many nearly orthogonal states with nearly minimal energy. For a physical discussion of the ‘many states’ aspect of the SK model, we refer to [26], Chapter III. A very interesting rigorous formulation was attempted by Talagrand (see [34], Conjecture 2.2.23), but no theorems were proved. Although our achievement is quite modest, and may not be satisfactory to the physicists because we do not prove that the approximate minimum energy states correspond to significantly large regions of the state space — in fact, one may say that it is not what is meant by the physical term ‘multiple valleys’ at all because an isolated low energy state does not necessarily represent a valley — it does seem that Theorem 1.1 is the first rigorous result about the multimodal geometry of the SherringtonKirkpatrick energy landscape. We may call it ‘multiple valleys in a weak sense’.
Theorem 1.1 can be generalized to the following Corollary, which shows that weak multiple valleys exist at ‘every energy level’ and not only for the lowest energy.
Corollary 1.2.
The variables in the Hamiltonian are collectively called the ‘couplings’ or the ‘disorder’. Our proof of Theorem 1.1 is based on the chaotic nature of the SK model under small perturbations of the couplings; this is discussed in the next subsection. The relation between chaos and multiple valleys follows from a general principle outlined in [7], although the proof in the present paper is selfcontained.
1.2. Disorder chaos in the SK model
Recall the Gibbs measure of the SK model, defined in (5). Suppose and are two configurations drawn independently according to the measure , and the overlap is defined as in (3). It is known that when , with high probability [15, 8, 34]. However, it is also known that cannot be concentrated near zero for all , because that would give a contradiction to the existence of a phase transition as established in [1]. In fact, it is believed that the limiting distribution of in the low temperature phase is given by the socalled ‘Parisi measure’, a notion first made rigorous by Talagrand [35, 36].
Now suppose we choose not from the Gibbs measure , but from a new Gibbs measure , based on a new Hamiltonian which is obtained by applying a small perturbation to the Hamiltonian . (We will make precise the notion of a small perturbation below.) Is it still true that has a nondegenerate limiting distribution at low temperatures? The conjecture of disorder chaos (i.e. chaos with respect to small fluctuations in the disorder ) states that indeed that is not the case: is concentrated near zero if is picked from the Gibbs measure and is picked from a perturbed Gibbs measure. This is supposed to be true at all temperatures. To the best of our knowledge, disorder chaos for the SK model was first discussed in the widely cited paper of Bray and Moore [5]; a related discussion appears in the earlier paper [25]. The phenomenon of chaos itself was first conjectured by Fisher and Huse [13] in the context of the EdwardsAnderson model, although the term was coined in [5]. Again, to the best of our knowledge, nothing has been proved rigorously yet. For further references in the physics literature, let us refer to the recent paper [24].
Note that this idea of chaos should not be confused with temperature chaos (also discussed in [5]), which says that spin glasses are chaotic with respect to small changes in the inverse temperature .
We shall consider two kinds of perturbation of the disorder. The first, what we call ‘discrete perturbation’, is executed by replacing a randomly chosen small fraction of the couplings by independent copies. Here small fraction means a fraction that goes to zero as . Discrete perturbation is the usual way to proceed in the noisesensitivity literature (see e.g. [3, 4, 31, 27, 16]). In fact, it seems that the following result is intimately connected with noisesensitivity, although we do not see any obvious way to use the standard noisesensitivity techniques to derive it.
Theorem 1.3.
Consider the SK model at inverse temperature . Take any and . Suppose a randomly chosen fraction of the couplings are replaced by independent copies to give a perturbed Gibbs measure. Let be chosen from the original Gibbs measure and is chosen from the perturbed measure. Let the overlap be defined as in (3). Then
where is an absolute constant and the expectation is taken over all randomness.
This theorem shows that the system is chaotic if the fraction goes to zero slower than . The derivation of this result is based on the ‘superconcentration’ property of the free energy in the SK model that we present in the next subsection.
The notion of perturbation in the above theorem, though natural, is not the only available notion. In fact, in the original physics papers (e.g. [5]), a different manner of perturbation is proposed, which we call continuous perturbation. Here we replace by , where is another set of indepenent standard Gaussian random variables and so that the resultant couplings are again standard Gaussian. When , we say that the perturbation is small. A convenient way to parametrize the perturbation is to set , where is a parameter that we call ‘time’. This nomenclature is natural, because perturbing the couplings up to time corresponds to running an OrnsteinUhlenbeck flow at each coupling for time , with initial value . The following theorem says that the SK model is chaotic under small continuous perturbations.
Theorem 1.4.
Consider the SK model at inverse temperature . Take any . Suppose we continuously perturb the couplings up to time , as defined above. Let be chosen from the original Gibbs measure and be chosen from the perturbed measure. Let the overlap be defined as in (3). Then there is an absolute constant such that for any positive integer ,
The expectation is taken over all randomness.
Again, the achievement is very modest, and does not come anywhere close to the claims of the physicists. But once again, this is the first rigorous result about chaos of any kind in the SK model. To the best of our knowledge, the only other instance of a rigorous proof of chaos in any spin glass model is in the work of Panchenko and Talagrand [30], who established chaos with respect to small changes in the external field in the spherical SK model. Disorder chaos in directed polymers was established by the author in [7].
A deficiency of both theorems in this subsection is that they do not cover the case of zero temperature, that is, , where Gibbs measure concentrates all its mass on the ground state. In principle, the same techniques should apply, but there are some crucial hurdles that cannot be cleared with the available ideas.
1.3. Superconcentration in the SK model
The notion of superconcentration was defined in [7]. The definition in [7] pertains only to maxima of Gaussian fields, but it can be generalized to roughly mean the following: a Lipschitz function of a collection of independent standard Gaussian random variables is superconcentrated whenever its order of fluctuations is much smaller than its Lipschitz constant. This definition is related to the classical concentration result for the Gaussian measure, which says that the order of fluctuations of a Lipschitz function under the Gaussian measure is bounded by its Lipschitz constant (see e.g. Theorem 2.2.4 in [34]), irrespective of the dimension.
The free energy of the SK model is defined as
(6) 
where is the Hamiltonian defined in (4). It follows from classical concentration of measure that the variance of is bounded by a constant multiple of (see Corollary 2.2.5 in [34]). This is the best known bound for . When , Talagrand (Theorems 2.2.7 and 2.2.13 in [34]) proved that the variance can actually be bounded by an absolute constant. This is also indicated in the earlier works of Aizenman, Lebowitz and Ruelle [1] and Comets and Neveu [8]. Therefore, according to our definition, the free energy is superconcentrated when . The following theorem shows that is superconcentrated at any .
Theorem 1.5.
Let be the free energy of the SK model defined above in (6). For any , we have
where is an absolute constant.
This result may be reminiscent of the improvement in the variance of first passage percolation time [4]. However, the proof is quite different in our case since hypercontractivity, the major tool in [4], does not seem to work for spin glasses in any obvious way. In that sense, the two results are quite unrelated. Our proof is based on our chaos theorem for continuous perturbation (Theorem 1.4) and ideas from [7]. On the other hand, Theorem 1.5 is used to derive the chaos theorem for discrete perturbation, again drawing upon ideas from [7]. This equivalence between chaos and superconcentration is one of the main themes of [7], which in a way shows the significance of superconcentration, which may otherwise be viewed as just a curious phenomenon.
Incidentally, it was shown by Talagrand ([37], eq. (10.13)) that the lower tail fluctuations of are actually as small as order under an unproven hypothesis about the Parisi measure.
1.4. Disorder chaos in the EA model
Let be an undirected graph. The EdwardsAnderson spin glass [11] on is defined through the Hamiltonian
(7) 
where is again a collection of i.i.d. random variables, often taken to be Gaussian. The SK model corresponds to the case of the complete graph, up to normalization by .
For a survey of the (few) rigorous and nonrigorous results available for the EdwardsAnderson model, we refer to Newman and Stein [28].
Unlike the SK model, there are two kinds of overlap in the EA model. The ‘site overlap’ is the usual overlap defined in (3). The ‘bond overlap’ between two states and , on the other hand, is defined as
(8) 
We show that the bond overlap in the EA model is not chaotic with respect to small fluctuations of the couplings at any temperature. This does not say anything about the site overlap; the site overlap in the EA model can well be chaotic with respect to small fluctuations of the couplings, as predicted in [13, 5].
Theorem 1.6.
Suppose the EA Hamiltonian (7) on a graph is continuously perturbed up to time , according to the definition of continuous perturbation in Section 1.2. Let be chosen from the original Gibbs measure at inverse temperature and is chosen from the perturbed measure. Let the bond overlap be defined as in (8). Let
where is the maximum degree of . Then
where is a positive absolute constant. Moreover, the result holds for also, with the interpretation that the Gibbs measure at is just the uniform distribution on the set of ground states.
An interesting case of the above theorem is when . The result then says that if two configurations are drawn independently from the Gibbs measure, they have a nonnegligible bond overlap with nonvanishing probability. The fact that this holds at any finite temperature is in contrast with the meanfield case (i.e. the SK model), where there is a hightemperature phase () where the bond overlap becomes negligible.
However, while Theorem 1.6 establishes that the bond overlap does not become zero for any amount of perturbation, it does exhibit a sort of ‘quenched chaos’, in the following sense.
Theorem 1.7.
Fix and let be as in Theorem 1.6. Then
That is, if we perturb the system by an amount , the bond overlap between two configurations drawn from the two Gibbs measures is approximately equal to the quenched average of the overlap. In physical terms, the overlap ‘selfaverages’.
The combination of the last two theorems brings to light a surprising phenomenon. On the one hand, the perturbation retains a memory of the original Gibbs measure, because the overlap is nonvanishing in Theorem 1.6. On the other hand, the perturbation causes a chaotic reorganization of the Gibbs measure in such a way that the overlap concentrates on a single value in Theorem 1.7. The author can see no clear explanation of this confusing outcome.
1.5. Absence of superconcentration in the EA model
The proof of Theorem 1.6 is based on the following result, which says that the free energy is not superconcentrated in the EA model on bounded degree graphs. This generalizes a wellknown result of Wehr and Aizenman [38], who proved the analogous result on square lattices. The relative advantage of our approach is that it does not use the structure of the graph, whereas the WehrAizenman proof depends heavily on properties of the lattice.
Theorem 1.8.
Let denote the free energy in the EdwardsAnderson model on a graph , defined in (6). Let be the maximum degree of . Then for any , including (where the free energy is just the energy of the ground state), we have
The above result is based on a formula (Theorem 3.11) for the variance of an arbitrary smooth function of Gaussian random variables.
1.6. A note about other models
It will clear from our proofs that the chaos and superconcentration results hold for the spin versions of the SK model for even . (See Chapter 6 of [34] for the definition of these models and various results.) In fact, a generalization of Theorem 1.4 is proven in Theorem 3.5 later, which includes the spin models for even .
It will also be clear that the lack of superconcentration is true in the Random Field Ising Model on general bounded degree graphs. (Again, the lattice case is handled in [38]. We refer to [38] for the definition of the RFIM.) The absence of superconcentration in the RFIM implies that the site overlap is stable under perturbations, instead of the bond overlap as in the EA model.
A simple model where our techniques give sharp results is the Random Energy Model (REM). This is discussed in Subsection 3.14.
1.7. Unsolved questions
In spite of the progress made in this paper over [7], many key issues are still out of reach. Some of them are as follows:

Another possible improvement to Theorem 1.1 can be achieved by increasing to something of the form .

Improve the superconcentration result (Theorem 1.5) so that the right hand side is for some . This is tied to the improvement of the chaos result.

If the above is not possible, at least prove a version of the superconcentration result where the right hand side does not depend on , or has a better dependence than . This will solve the question of chaos for .

Prove that the site overlap in the EdwardsAnderson model is chaotic with respect to fluctuations in the disorder, even though the bond overlap is not.

Prove disorder chaos in the SK model with nonzero external field, that is, if there is an additional term of the form in the Hamiltonian. The general nature of the SK model indicates that any result for may be substantially harder to prove than for . (Reportedly, a sketch of the proof in this case will appear in the new edition of [34].)

Show that in the EA model, the variance of tends to zero and the graph size goes to infinity.

Establish temperature chaos in any of these models.
The rest of the paper is organized as follows. In Section 2, we sketch the proofs of the main results. In Section 3, we present some general results that cover a wider class of Gaussian fields. All proofs are given in Section 3.
2. Proof sketches
In this section we give very short sketches of some of the main ideas of this paper.
2.1. Multiple valleys from chaos
Suppose we choose from the Gibbs measure at inverse temperature and from the measure obtained by applying a continuous perturbation up to time . Let and be the two Hamiltonians. Suppose and sufficiently slowly so that chaos holds (i.e. as ). Clearly this is possible by Theorem 1.4. Then due to chaos, and are approximately orthogonal. Since , nearly minimizes and nearly minimizes . But, since , . Thus, and both nearly minimize . This procedure finds two states that have nearly minimal energy and are nearly orthogonal. Repeating this procedure, we find many such states. The details are of this argument are worked out in Subsection 3.3.
2.2. Superconcentration iff chaos under continuous perturbations
Let denote when is drawn from the unperturbed Gibbs measure at inverse temperature and is drawn from the Gibbs measure continuously perturbed up to time . Let be the free energy defined in (6). Then we show that
(9) 
The proof of this result (Theorem 3.8) is simply a combination of the heat equation for the OrnsteinUhlenbeck process and integrationbyparts. The formula directly shows that whenever falls of sharply to zero, which is a way of saying that chaos implies superconcentration.
In Subsection 3.1, we show that is a nonnegative and decreasing function. This proves the converse implication, since the integral of a nonnegative decreasing function can be small only if the function drops off sharply to zero.
2.3. Chaos under continuous perturbations
Suppose is drawn from the Gibbs measure of the SK model at inverse temperature , and from the measure continuously perturbed up to time . Let be the overlap of and , as usual, and let
We have to show that for all ,
where is some constant that depends only on .
By repeated applications of differentiation and Gaussian integrationbyparts, we show that for all and . Here denotes the th derivative of . Such functions are called completely monotone. Now, by a classical theorem of Bernstein about completely monotone functions, there is a probability measure on such that
(10) 
By Hölder’s inequality and the above representation, it follows that for ,
In other words, chaos under large perturbations implies chaos under small perturbations. Thus, it suffices to prove that for sufficiently large .
The next step is an ‘induction from infinity’. It is not difficult to see that when , after integrating out the disorder, and are independent and uniformly distributed on . From this it follows that . We use this to obtain a similar bound on for sufficiently large , through the following steps. First, we show that for any and ,
Thus, we have a chain of differential inequalities. It is possible to manipulate this chain to conclude that
The right hand side is bounded by if and only if is sufficiently large. (This is related to the fact that when is a standard Gaussian random variable, if and only if .) This completes the proof sketch. The details of the above argument are worked out in Subsection 3.1.
2.4. Chaos in EA model
The proof of Theorem 1.6, again, is based on the representation (9) of the variance of the free energy and the representation (10) of the function (both of which hold for the EA model as well). From (10), it follows that there is a nonnegative random variable such that for all ,
From this and (9) it follows that
Next, we prove a simple analytical fact: Suppose is a nonnegative random variable and let . Then for any ,
Using this inequality for the random variable and the lower bound on the variance from Theorem 1.8, it is easy to obtain the required lower bound on the function , which establishes the absence of chaos. The details of this argument are presented in Subsection 3.7.
The proof of Theorem 1.7 involves a new idea. Let , and let be independent copies of . For each , let
For each , let denote a configuration drawn from the Gibbs measure defined by the disorder . For , we assume that and are independent given . Define
By a similar logic as in the derivation of (10), one can show that is a completely monotone function. Also, is bounded by . Thus, for any ,
(11) 
Now fix and let
It turns out that
and
where is the bond overlap between and . Combining these two identities with the inequality (11), it is easy to complete the proof of Theorem 1.7. The details are in Subsection 3.11.
2.5. Chaos under discrete perturbations
Let , and let be an independent copy of . For any , let be the array whose th component is
Let be the free energy, considered as a function of . Suppose and are constants such that for all ,
Fix , and let be a subset of , chosen uniformly at random from the collection of all subsets of size . Let be chosen from the Gibbs measure at inverse temperature defined by the disorder , and let be drawn from the Gibbs measure defined by . Let denote the overlap of and , as usual. The key step is to prove that for some absolute constant ,
This inequality is the content of Theorem 3.14. The proof is completed by showing that we can choose and such that , and using the superconcentration bound (Theorem 1.5) on the variance of . The details of the proof are given in Subsection 3.12.
2.6. No superconcentration in the EA model
Although this result was already proven in [38] for the EA model on lattices, it may be worth sketching our argument for general bounded degree graphs here. Our proof is based on a general lower bound for arbitrary functions of Gaussian random variables. The result (Theorem 3.12) goes as follows: Suppose is an absolutely continuous function such that there is a version of its gradient that is bounded on bounded sets. Let be a standard Gaussian random vector in , and suppose and are both finite. Then
where denotes the usual inner product on . We apply this result to the Gaussian vector , taking the function to be the free energy . A few tricks are required to get a lower bound on the right hand side that does not blow up as .
Incidentally, the above lower bound on the variance of Gaussian functionals is based on a multidimensional Plancherel formula that may be of independent interest:
(12) 
Versions of this formula have been previously derived in the literature using expansions with respect to the multivariate orthogonal Hermite polynomial basis (see Subsection 3.8 for references). We give a different proof avoiding the use of the orthogonal basis.
3. General results about Gaussian fields and proofs
The results of Section 1 are applications of some general theorems about Gaussian fields. These are presented in this section, together with the proofs of the theorems of Section 1. Unlike the previous sections, we proceed according to the theoremproof format in the rest of the paper.
3.1. Chaos in Gaussian fields
Let be a finite set and let be a centered Gaussian random vector. Let
Let be an independent copy of , and for each , let
Fix . For each , define a probability measure on that assigns mass
to the point , for each . The average of a function under the measure will be denoted by , that is,
We will consider the covariance kernel as a function on , defined as . Alternatively, it will also be considered as a square matrix.
Theorem 3.1.
Assume that for all . For each , let
Let be any convergent power series on all of whose coefficients are nonnegative. Then for each ,
Moreover, is a decreasing function of .
Roughly, the way to apply this theorem is the following: prove that the right hand side is small for some large using high temperature methods, and then use the infimum to show that the smallness persists for small as well.
Since the application of Theorem 3.1 to the SK model seems to yield a suboptimal result (Theorem 1.4), one can question whether Theorem 3.1 can ever give sharp bounds. In Subsection 3.14 we settle this issue by showing that Theorem 3.1 gives a sharp result for Derrida’s Random Energy Model.
Let us now proceed to prove Theorem 3.1. In the following, will denote the set of all infinitely differentiable realvalued functions on with bounded derivatives of all orders.
Let us first extend the definition of to negative . This is done quite simply. Let be another independent copy of that is also independent of , and for each , let
Let us now recall Gaussian integration by parts: If is an absolutely continuous function such that has finite expectation, then for any ,
where denotes the partial derivative of along the th coordinate (see e.g. [34], Appendix A.6). The following lemma is simply a reformulated version of the above identity.
Lemma 3.2.
For any , we have
Proof.
For each , define
A simple computation gives
(Note that issues like moving derivatives inside expectations are easily taken care of due to the assumption that .) One can verify by computing covariances that and the pair are independent. Moreover,
So for any , Gaussian integration by parts gives
The proof is completed by combining the last two steps. ∎
Our next lemma is the most crucial component of the whole argument. It gives a way of extrapolating high temperature results to the low temperature regime.
Lemma 3.3.
Let be the class of all functions on that can be expressed as
for some nonnegative integer and nonnegative real numbers , and functions in . For any , there is a probability measure on such that for each ,
In particular, for any ,
Proof.
Note that any must necessarily be a nonnegative function, since and are independent and identically distributed conditional on , which gives
Now, if , then for all , and there is nothing to prove. So let us assume .
Since is a positive semidefinite matrix, there is a square matrix such that . Thus, given a function , if we define
then by Lemma 3.2 we have
From this observation and the definition of , it follows easily that if , then . Proceeding by induction, we see that for any , is a nonnegative function (where denotes the th derivative of ). Such functions on are called ‘completely monotone’. The most important property of completely monotone functions (see e.g. Feller [12], Vol. II, Section XIII.4) is that any such function can be represented as the Laplace transform of a positive Borel measure on , that is,
Moreover, . By taking , this proves the first assertion of the theorem. For the second, note that by Hölder’s inequality, we have that for any ,
This completes the proof. ∎
The next lemma is obtained by a variant of the Gaussian interpolation methods for analyzing mean field spin glasses at high temperatures. It is similar to R. Latała’s unpublished proof of the replica symmetric solution of the SK model (to appear in the new edition of [34]).
Lemma 3.4.
Let and be as in Theorem 3.1. Then for each ,
Proof.
For each , define a function as
Note that
where if and otherwise. Since is bounded, this proves in particular that .
Take any nonnegative integer . Since is a positive semidefinite matrix, so is . (To see this, just note that are independent copies of , then .) Therefore there exists a matrix such that . Define the functions
In the following we will denote and by and respectively, for all . Let
By Lemma 3.2 we get