Estimation in the Spiked Wigner Model: A Short Proof of the Replica Formula

by   Ahmed El Alaoui, et al.
berkeley college

We consider the problem of estimating the rank-one perturbation of a Wigner matrix in a setting of low signal-to-noise ratio. This serves as a simple model for principal component analysis in high dimensions. The mutual information per variable between the spike and the observed matrix, or equivalently, the normalized Kullback-Leibler divergence between the planted and null models are known to converge to the so-called replica-symmetric formula, the properties of which determine the fundamental limits of estimation in this model. We provide in this note a short and transparent proof of this formula, based on simple executions of Gaussian interpolations and standard concentration-of-measure arguments. The Franz-Parisi potential, that is, the free entropy at a fixed overlap, plays an important role in our proof. Our proof can be generalized straightforwardly to spiked tensor models of even order.



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1 Introduction

Extracting low-rank information from a data matrix corrupted with noise is a fundamental statistical task. Spiked random matrix models have attracted considerable attention in statistics, probability and machine learning as rich testbeds for theoretical investigation on this problem 

[Joh01, Péc14, Péc06, BAP05]. A basic such model is the spiked Wigner model in which one observes a rank-one deformation of a Wigner matrix :


where and are independent for all . The spikevector represents the signal to be recovered, and plays the role of a Signal-to-Noise Ratio (SNR) parameter. The entries of come i.i.d. from a (Borel) prior on with bounded support, so that the scaling in the above model puts the problem in a high-noise regime where only partial recovery of the spike is possible. A basic statistical question about this model is for what values of the SNR is it possible to estimate the spike with non-trivial accuracy?

Spectral methods, or more precisely, estimation using the top eigenvector of

, are know to succeed above a spectral threshold and fail below [BGN11]. Since the posterior mean is the estimator with minimal mean squared error, this question boils down to the study of the posterior distribution of given , which by Bayes’ rule, can be written as


where is the (random) Hamiltonian


Let us define the free entropy111The term “free energy” is also used, although the physics convention requires to put a minus sign in front of the expression in this case. of the model as the expected log-partition function (i.e., normalizing constant) of the posterior :


By heuristically analyzing an approximate message passing (AMP) algorithm for this problem, Lesieur et al. 

[LKZ15] derived an asymptotic—so-called replica-symmetric ()—formula for the above quantity. This formula is defined as follows: for , let

where and are mutually independent. Define the potential

The conjectured limit of (4) is the formula

This conjecture was then proved shortly after in a series of papers [KXZ16, BDM16, DAM16, LM16] (see also [KM09]):

Theorem 1.

For all ,

The above statement contains precious statistical information. It can be written in at least two other equivalent ways, in terms of the mutual information between and :

or, denoting by

the probability distribution of the matrix

as per (1), in terms of the Kullback-Liebler divergence between and :

Furthermore, the point achieving the maximum in the formula (which can be shown to be unique and finite for almost every ) can be interpreted as the best overlap any estimator can have with the spike . Indeed, the overlap of a draw from the posterior with concentrates about . See [BDM16, LM16, EKJ17] for various forms of this statement.

2 Comment on the existing proofs

The proof of the lower bound relies on an application of Guerra’s interpolation method [Gue01, GT02], and is fairly short and transparent. (See [KXZ16].) Available proofs of the converse bound (as well as overlap concentration) are on the other hand highly involved. Barbier et al. [BDM16] and Deshpande et al. [DAM16] adopt an algorithmic approach: they analyze an approximate message passing procedure and show that the produced estimator asymptotically achieves an overlap of

with the spike. Thus the posterior mean, being the optimal estimator, must also achieve the same overlap. This allows to prove overlap convergence and thus show the converse bound. A difficulty one has to overcome with this method is that AMP (and supposedly any other algorithm) may fail to achieve the optimal overlap in the presence of first-order phase transitions, which traps the algorithm in a bad local optimum of the

potential. Spatial coupling, an idea from coding theory, is used in [BDM16] to overcome this problem. Lelarge and Miolane [LM16] on the other hand use the Aizenman-Sims-Starr scheme [ASS03], a relative of the cavity method developed within spin-glass theory, to prove the upper bound. Barbier and Macris [BM17] prove the upper bound via a adaptive version of the interpolation method that proceeds via a sequence of intermediate interpolation steps. Recently, the optimal rate of convergence and constant order corrections to the formula were proved in [EKJ17] using a rigorous incarnation of the cavity method due to Talagrand [Tal11]. However, all the current approaches (perhaps to a lesser extent for [BM17]) require the execution of long and technical arguments.

In this note, we show that the upper bound in Theorem 1 admits a fairly simple proof based on the same interpolation idea that yielded the lower bound, combined with an application of the Laplace method and concentration of measure. The main idea is to consider a version of the free entropy (4) of a subsystem of configurations having a fixed overlap with the spike . We then proceed by applying the Guerra bound and optimize over this fixed overlap (which is a free parameter) to obtain an upper bound in the form of a saddle (max-min) formula. A small extra effort is needed to show that this last formula is another representation of the formula. The idea of restricting the overlap dates back to Franz and Parisi [FP95, FP98] who introduced it in order to study the relaxation properties of dynamics in spin-glass models. The free entropy at fixed overlap bears the name of the Franz-Parisi potential. Our proof thus hinges on a upper bound on this potential, which is may be of independent interest. We first start by presenting the proof of the lower bound, which is a starting point for our argument. We present the proof in the case , i.e., we omit the diagonal terms of . This is only done to keep the displays concise; recovering the general case is straightforward since the diagonal has vanishing contribution to the overall free entropy. Finally, the method presented here can be easily generalized to all spiked tensor models of even order [RM14], thus recovering the main results of [LML17].

3 Proof of Theorem 1

Let and consider an interpolating Hamiltonian


where the ’s are i.i.d. standard Gaussian r.v.’s independent of everything else. For , we define the Gibbs average of as


This is the average of with respect to the posterior distribution of copies of given the augmented set of observations


The variables are called replicas

, and are interpreted as random variables independently drawn from the posterior. When

we simply write instead of . We shall denote the overlaps between two replicas as follows: for , we let

A simple consequence of Bayes’ rule is that the -tuples and have the same law under (see Proposition 16 in [LM16]). This bears the name of the Nishimori property in the spin glass literature [Nis01].

3.1 The lower bound

Reproducing the argument of [KXZ16], we prove using Guerra’s interpolation [Gue01] and the Nishimori property that

We let in the definition of and let

A short calculation based on Gaussian integration by parts shows that

By the Nishimori property, the expressions involving the pairs on the one hand and on the other in the brackets are equal. We then obtain

Observe that the last term is since the variables are bounded. Moreover, the first term is always non-negative so we obtain

Since and , integrating over , we obtain for all , and this yields the lower bound.

3.2 The upper bound

We prove the converse bound

We introduce the Franz-Parisi potential [FP95, FP98]. For fixed, and we define

where the expectation is over . This is the free entropy of a subsystem of configurations having an overlap close to a fixed value with a planted signal . It is clear that . We will argue via the Laplace method and concentration of measure that , then use Guerra’s interpolation to upper bound (notice that this method yielded a lower bound on due to the Nishimori property). Let us define a bit of more notation. For , let

where , and where . Moreover, let

and similarly define .

Proposition 2.

There exist such that for all , we have

Now we upper bound in terms of :

Proposition 3 (Interpolation upper bound).

There exist depending on such that for all and we have

Remark: This simple upper bound on the Franz-Parisi potential—which may be of independent interest—can be straightforwardly generalized to spiked tensor models of even order. Indeed, as will be apparent from the proof in the present matrix case, a crucial step in obtaining the inequality is the positivity of a certain hard-to-control remainder term222We note that the adaptive interpolation method of Barbier and Macris [BM17] is able to bypass this issue of positivity of the remainder term along the interpolation path, as long as this interpolation “stays on the Nishimori line”, i.e., corresponds to an inference problem for every

(this is however not true in the case of the FP potential.) They are thus able to compute the free entropy of (asymmetric) spiked tensor models of odd order. See 

[BMM17, BKM17].. Tensor models of even order enjoy a convexity property that ensures the positivity of this remainder.

From Propositions 2 and 3, an upper bound on in the form of a saddle formula begins to emerge. For a fixed let be any minimizer of on . (By differentiating , we can check that is bounded uniformly in .) Then we have


At this point we need to push the expectation inside the supremum. This will be done using a concentration argument.

Lemma 4.

There exists such that for all , , and ,

It is a routine computation to deduce from Lemma 4 (and boundedness of both and ) that the expected supremum is bounded by the supremum of the expectation plus a small term (a similar argument is given in the proof of Proposition 2):

where . Since is a minimizer of , it follows from (8) that


We now let , and conclude by noticing that the above saddle formula is another expression for :

Proposition 5.


One inequality follows from (9) and the lower bound . For the converse inequality, we notice that for all

Now we use the fact that the function is largest when its second argument is positive:

Lemma 6.

For all we have

This implies Taking the supremum over yields the converse bound.

Proof of Proposition 2. Let . Since the prior has bounded support, we can grid the set of the overlap values by many intervals of size for some . This allows the following discretization, where runs over the finite range :


In the above, is w.r.t. both and . We use concentration of measure to push the expectation over to the left of the maximum. Let

We show that each term concentrates about its expectation (in the randomness of ). Let denote the expectation w.r.t. .

Lemma 7.

There exists a constant such that for all and all ,

Therefore, the expectation of the maximum concentrates as well:

We set and obtain

Therefore, plugging the above estimates into (10), we obtain


Proof of Proposition 3. Let and consider a slightly modified interpolating Hamiltonian that has two parameters and :


where the ’s are i.i.d. standard Gaussian r.v.’s independent of everything else. Let

where is over the Gaussian disorder and ( is fixed). Let be the corresponding Gibbs average, similarly to (6). By differentiation and Gaussian integration by parts,

Notice that by the overlap restriction, . Moreover, the last terms in the first and second lines in the above are of order since the variables are bounded. Next, since has non-negative sign (this is a crucial fact), we can ignore it and obtain an upper bound:

Integrating over , we obtain

Now we use a trivial upper bound on :



Proof of lemma 4. The random part of is the average of i.i.d. terms . Since , and , where is a bound on the support of , we have . For bounded and , the claim follows from concentration of the average of i.i.d. bounded r.v.’s.  

Proof of Lemma 7. We notice that seen as a function of is Lipschitz with constant . By Gaussian concentration of Lipschitz functions (the Tsirelson-Ibragimov-Sudakov inequality [BLM13]), there exist a constant depending only on such that for all ,

Then we conclude by means of the identity

and integrate the tail.  

Proof of Lemma 6. Let , and let be the symmetric part of , i.e., for all Borel . Observe that is absolutely continuous with respect to . The argument relies on a linearly interpolating between the two measures and . Let and let . Further, let be fixed, and

where . Now let . We have on the one hand, and since is a symmetric distribution, on the other. We will show that is a convex increasing function on the interval . Then we deduce that . First, we have


Similar expressions holds for where is replaced by inside the exponentials. We see from the expression of the first derivative at that . This is because is symmetric about the origin, so a sign change (of for the first term, and for the second term) does not affect the value of the integrals. Hence . Now, we focus on the second derivative. Observe that since is the symmetric part of , is anti-symmetric. This implies that the first term in the expression of the second derivative changes sign under a sign change in and keeps the same modulus. As for the second term, a sign change in induces integration against . Hence we can write the difference as

For any Borel , we have . Therefore the second term in the above expression becomes

Since both and are absolutely continuous with respect to for all we can write

where the Gibbs average is with respect to the posterior of given under the Gaussian channel , and the expectation is under and . By the Nishimori property, we simplify the above expression to

where the expression is valid for all . From here we see that the function is convex on . Since , is also increasing on .  

Acknowledgments. Florent Krzakala acknowledges funding from the ERC under the European Union 7th Framework Programme Grant Agreement 307087-SPARCS.


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