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Van Vu and I have just uploaded to the arXiv our new paper, “Random matrices: Universality of ESDs and the circular law“, with an appendix by Manjunath Krishnapur (and some numerical data and graphs by Philip Wood). One of the things we do in this paper (which was our original motivation for this project) was to finally establish the endpoint case of the circular law (in both strong and weak forms) for random iid matrices , where the coefficients are iid random variables with mean zero and unit variance. (The strong circular law says that with probability 1, the empirical spectral distribution (ESD) of the normalised eigenvalues of the matrix converges to the uniform distribution on the unit circle as . The weak circular law asserts the same thing, but with convergence in probability rather than almost sure convergence; this is in complete analogy with the weak and strong law of large numbers, and in fact this law is used in the proof.) In a previous paper, we had established the same claim but under the additional assumption that the moment was finite for some ; this builds upon a significant body of earlier work by Mehta, Girko, Bai, Bai-Silverstein, Gotze-Tikhomirov, and Pan-Zhou, as discussed in the blog article for the previous paper.
As it turned out, though, in the course of this project we found a more general universality principle (or invariance principle) which implied our results about the circular law, but is perhaps more interesting in its own right. Observe that the statement of the circular law can be split into two sub-statements:
- (Universality for iid ensembles) In the asymptotic limit , the ESD of the random matrix is independent of the choice of distribution of the coefficients, so long as they are normalised in mean and variance. In particular, the ESD of such a matrix is asymptotically the same as that of a (real or complex) gaussian matrix with the same mean and variance.
- (Circular law for gaussian matrices) In the asymptotic limit , the ESD of a gaussian matrix converges to the circular law.
The reason we single out the gaussian matrix ensemble is that it has a much richer algebraic structure (for instance, the real (resp. complex) gaussian ensemble is invariant under right and left multiplication by the orthogonal group O(n) (resp. the unitary group U(n))). Because of this, it is possible to compute the eigenvalue distribution very explicitly by algebraic means (for instance, using the machinery of orthogonal polynomials). In particular, the circular law for complex gaussian matrices (Statement 2 above) was established all the way back in 1967 by Mehta, using an explicit formula for the distribution of the ESD in this case due to Ginibre.
These highly algebraic techniques completely break down for more general iid ensembles, such as the Bernoulli ensemble of matrices whose entries are +1 or -1 with an equal probability of each. Nevertheless, it is a remarkable phenomenon – which has been referred to as universality in the literature, for instance in this survey by Deift – that the spectral properties of random matrices for non-algebraic ensembles are in many cases asymptotically indistinguishable in the limit from that of algebraic ensembles with the same mean and variance (i.e. Statement 1 above). One might view this as a sort of “non-Hermitian, non-commutative” analogue of the universality phenomenon represented by the central limit theorem, in which the limiting distribution of a normalised average
of an iid sequence depends only on the mean and variance of the elements of that sequence (assuming of course that these quantities are finite), and not on the underlying distribution. (The Hermitian non-commutative analogue of the CLT is known as Wigner’s semicircular law.)
Previous approaches to the circular law did not build upon the gaussian case, but instead proceeded directly, in particular controlling the ESD of a random matrix via estimates on the Stieltjes transform
of that matrix for complex numbers z. This method required a combination of delicate analysis (in particular, a bound on the least singular values of ), and algebra (in order to compute and then invert the Stieltjes transform). [As a general rule, and oversimplifying somewhat, algebra tends to be used to control main terms in a computation, while analysis is used to control error terms.]
What we discovered while working on our paper was that the algebra and analysis could be largely decoupled from each other: that one could establish a universality principle (Statement 1 above) by relying primarily on tools from analysis (most notably the bound on least singular values mentioned earlier, but also Talagrand’s concentration of measure inequality, and a universality principle for the singular value distribution of random matrices due to Dozier and Silverstein), so that the algebraic heavy lifting only needs to be done in the gaussian case (Statement 2 above) where the task is greatly simplified by all the additional algebraic structure available in that setting. This suggests a possible strategy to proving other conjectures in random matrices (for instance concerning the eigenvalue spacing distribution of random iid matrices), by first establishing universality to swap the general random matrix ensemble with an algebraic ensemble (without fully understanding the limiting behaviour of either), and then using highly algebraic tools to understand the latter ensemble. (There is now a sophisticated theory in place to deal with the latter task, but the former task – understanding universality – is still only poorly understood in many cases.)