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We can now turn attention to one of the centerpiece universality results in random matrix theory, namely the Wigner semi-circle law for Wigner matrices. Recall from previous notes that a Wigner Hermitian matrix ensemble is a random matrix ensemble {M_n = (\xi_{ij})_{1 \leq i,j \leq n}} of Hermitian matrices (thus {\xi_{ij} = \overline{\xi_{ji}}}; this includes real symmetric matrices as an important special case), in which the upper-triangular entries {\xi_{ij}}, {i>j} are iid complex random variables with mean zero and unit variance, and the diagonal entries {\xi_{ii}} are iid real variables, independent of the upper-triangular entries, with bounded mean and variance. Particular special cases of interest include the Gaussian Orthogonal Ensemble (GOE), the symmetric random sign matrices (aka symmetric Bernoulli ensemble), and the Gaussian Unitary Ensemble (GUE).

In previous notes we saw that the operator norm of {M_n} was typically of size {O(\sqrt{n})}, so it is natural to work with the normalised matrix {\frac{1}{\sqrt{n}} M_n}. Accordingly, given any {n \times n} Hermitian matrix {M_n}, we can form the (normalised) empirical spectral distribution (or ESD for short)

\displaystyle  \mu_{\frac{1}{\sqrt{n}} M_n} := \frac{1}{n} \sum_{j=1}^n \delta_{\lambda_j(M_n) / \sqrt{n}},

of {M_n}, where {\lambda_1(M_n) \leq \ldots \leq \lambda_n(M_n)} are the (necessarily real) eigenvalues of {M_n}, counting multiplicity. The ESD is a probability measure, which can be viewed as a distribution of the normalised eigenvalues of {M_n}.

When {M_n} is a random matrix ensemble, then the ESD {\mu_{\frac{1}{\sqrt{n}} M_n}} is now a random measure – i.e. a random variable taking values in the space {\hbox{Pr}({\mathbb R})} of probability measures on the real line. (Thus, the distribution of {\mu_{\frac{1}{\sqrt{n}} M_n}} is a probability measure on probability measures!)

Now we consider the behaviour of the ESD of a sequence of Hermitian matrix ensembles {M_n} as {n \rightarrow \infty}. Recall from Notes 0 that for any sequence of random variables in a {\sigma}-compact metrisable space, one can define notions of convergence in probability and convergence almost surely. Specialising these definitions to the case of random probability measures on {{\mathbb R}}, and to deterministic limits, we see that a sequence of random ESDs {\mu_{\frac{1}{\sqrt{n}} M_n}} converge in probability (resp. converge almost surely) to a deterministic limit {\mu \in \hbox{Pr}({\mathbb R})} (which, confusingly enough, is a deterministic probability measure!) if, for every test function {\varphi \in C_c({\mathbb R})}, the quantities {\int_{\mathbb R} \varphi\ d\mu_{\frac{1}{\sqrt{n}} M_n}} converge in probability (resp. converge almost surely) to {\int_{\mathbb R} \varphi\ d\mu}.

Remark 1 As usual, convergence almost surely implies convergence in probability, but not vice versa. In the special case of random probability measures, there is an even weaker notion of convergence, namely convergence in expectation, defined as follows. Given a random ESD {\mu_{\frac{1}{\sqrt{n}} M_n}}, one can form its expectation {{\bf E} \mu_{\frac{1}{\sqrt{n}} M_n} \in \hbox{Pr}({\mathbb R})}, defined via duality (the Riesz representation theorem) as

\displaystyle  \int_{\mathbb R} \varphi\ d{\bf E} \mu_{\frac{1}{\sqrt{n}} M_n} := {\bf E} \int_{\mathbb R} \varphi\ d	 \mu_{\frac{1}{\sqrt{n}} M_n};

this probability measure can be viewed as the law of a random eigenvalue {\frac{1}{\sqrt{n}}\lambda_i(M_n)} drawn from a random matrix {M_n} from the ensemble. We then say that the ESDs converge in expectation to a limit {\mu \in \hbox{Pr}({\mathbb R})} if {{\bf E} \mu_{\frac{1}{\sqrt{n}} M_n}} converges the vague topology to {\mu}, thus

\displaystyle  {\bf E} \int_{\mathbb R} \varphi\ d	 \mu_{\frac{1}{\sqrt{n}} M_n} \rightarrow \int_{\mathbb R} \varphi\ d\mu

for all {\phi \in C_c({\mathbb R})}.

In general, these notions of convergence are distinct from each other; but in practice, one often finds in random matrix theory that these notions are effectively equivalent to each other, thanks to the concentration of measure phenomenon.

Exercise 1 Let {M_n} be a sequence of {n \times n} Hermitian matrix ensembles, and let {\mu} be a continuous probability measure on {{\mathbb R}}.

  • Show that {\mu_{\frac{1}{\sqrt{n}} M_n}} converges almost surely to {\mu} if and only if {\mu_{\frac{1}{\sqrt{n}}}(-\infty,\lambda)} converges almost surely to {\mu(-\infty,\lambda)} for all {\lambda \in {\mathbb R}}.
  • Show that {\mu_{\frac{1}{\sqrt{n}} M_n}} converges in probability to {\mu} if and only if {\mu_{\frac{1}{\sqrt{n}}}(-\infty,\lambda)} converges in probability to {\mu(-\infty,\lambda)} for all {\lambda \in {\mathbb R}}.
  • Show that {\mu_{\frac{1}{\sqrt{n}} M_n}} converges in expectation to {\mu} if and only if {\mathop{\mathbb E} \mu_{\frac{1}{\sqrt{n}}}(-\infty,\lambda)} converges to {\mu(-\infty,\lambda)} for all {\lambda \in {\mathbb R}}.

We can now state the Wigner semi-circular law.

Theorem 1 (Semicircular law) Let {M_n} be the top left {n \times n} minors of an infinite Wigner matrix {(\xi_{ij})_{i,j \geq 1}}. Then the ESDs {\mu_{\frac{1}{\sqrt{n}} M_n}} converge almost surely (and hence also in probability and in expectation) to the Wigner semi-circular distribution

\displaystyle  \mu_{sc} := \frac{1}{2\pi} (4-|x|^2)^{1/2}_+\ dx. \ \ \ \ \ (1)

A numerical example of this theorem in action can be seen at the MathWorld entry for this law.

The semi-circular law nicely complements the upper Bai-Yin theorem from Notes 3, which asserts that (in the case when the entries have finite fourth moment, at least), the matrices {\frac{1}{\sqrt{n}} M_n} almost surely has operator norm at most {2+o(1)}. Note that the operator norm is the same thing as the largest magnitude of the eigenvalues. Because the semi-circular distribution (1) is supported on the interval {[-2,2]} with positive density on the interior of this interval, Theorem 1 easily supplies the lower Bai-Yin theorem, that the operator norm of {\frac{1}{\sqrt{n}} M_n} is almost surely at least {2-o(1)}, and thus (in the finite fourth moment case) the norm is in fact equal to {2+o(1)}. Indeed, we have just shown that the circular law provides an alternate proof of the lower Bai-Yin bound (Proposition 11 of Notes 3).

As will hopefully become clearer in the next set of notes, the semi-circular law is the noncommutative (or free probability) analogue of the central limit theorem, with the semi-circular distribution (1) taking on the role of the normal distribution. Of course, there is a striking difference between the two distributions, in that the former is compactly supported while the latter is merely subgaussian. One reason for this is that the concentration of measure phenomenon is more powerful in the case of ESDs of Wigner matrices than it is for averages of iid variables; compare the concentration of measure results in Notes 3 with those in Notes 1.

There are several ways to prove (or at least to heuristically justify) the circular law. In this set of notes we shall focus on the two most popular methods, the moment method and the Stieltjes transform method, together with a third (heuristic) method based on Dyson Brownian motion (Notes 3b). In the next set of notes we shall also study the free probability method, and in the set of notes after that we use the determinantal processes method (although this method is initially only restricted to highly symmetric ensembles, such as GUE).

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