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Van Vu and I have just uploaded to the arXiv our survey paper “From the Littlewood-Offord problem to the Circular Law: universality of the spectral distribution of random matrices“, submitted to Bull. Amer. Math. Soc..  This survey recaps (avoiding most of the technical details) the recent work of ourselves and others that exploits the inverse theory for the Littlewood-Offord problem (which, roughly speaking, amounts to figuring out what types of random walks exhibit concentration at any given point), and how this leads to bounds on condition numbers, least singular values, and resolvents of random matrices; and then how the latter then leads to universality of the empirical spectral distributions (ESDs) of random matrices, and in particular to the circular law for the ESDs for iid random matrices with zero mean and unit variance (see my previous blog post on this topic, or my Lewis lectures).  We conclude by mentioning a few open problems in the subject.

While this subject does unfortunately contain a large amount of technical theory and detail, every so often we find a very elementary observation that simplifies the work required significantly.  One such observation is an identity which we call the negative second moment identity, which I would like to discuss here.    Let A be an $n \times n$ matrix; for simplicity we assume that the entries are real-valued.  Denote the n rows of A by $X_1,\ldots,X_n$, which we view as vectors in ${\Bbb R}^n$.  Let $\sigma_1(A) \geq \ldots \geq \sigma_n(A) \geq 0$ be the singular values of A. In our applications, the vectors $X_j$ are easily described (e.g. they might be randomly distributed on the discrete cube $\{-1,1\}^n$), but the distribution of the singular values $\sigma_j(A)$ is much more mysterious, and understanding this distribution is a key objective in this entire theory.

This is my second Milliman lecture, in which I talk about recent applications of ideas from additive combinatorics (and in particular, from the inverse Littlewood-Offord problem) to the theory of discrete random matrices.
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Van Vu and I have recently uploaded our joint paper, “Random matrices: the circular law“, submitted to Contributions to Discrete Mathematics. In this paper we come close to fully resolving the circular law conjecture regarding the eigenvalue distribution of random matrices, for arbitrary choices of coefficient distribution.

More precisely, suppose we have an $n \times n$ matrix $N_n$ for some large n, where each coefficient $x_{ij}$ of $N_n$ is an independent identically distributed copy of a single random variable x (possibly complex-valued). x could be continuous (e.g. a Gaussian) or discrete (e.g. a Bernoulli random variable, taking values +1 and -1 with equal probability). For simplicity, let us normalise x to have mean 0 and variance 1 (in particular, the second moment is finite). This matrix will not be self-adjoint or normal, but we still expect it to be diagonalisable, with n complex eigenvalues. Heuristic arguments suggest that these eigenvalues should mostly have magnitude $O(\sqrt{n})$; for instance, one can see this by observing that the Hilbert-Schmidt norm (a.k.a. the Frobenius norm) $\hbox{tr} N_n^* N_n$, which can be shown to dominate the sum of squares of the magnitudes of the eigenvalues, is of size comparable to $n^2$ on the average. Because of this, it is customary to normalise the matrix by $1/\sqrt{n}$; thus let $\lambda_1,\ldots,\lambda_n$ be the n complex eigenvalues of $\frac{1}{\sqrt{n}} N_n$, arranged in any order.

Numerical evidence (as seen for instance here) soon reveals that these n eigenvalues appear to distribute themselves uniformly in the unit circle $\{ z \in {\Bbb C}: |z| \leq 1 \}$ in the limit $n \to \infty$. This phenomenon is known as the circular law. It can be made more precise; if we define the empirical spectral distribution $\mu_n: {\Bbb R}^2 \to [0,1]$ to be the function

$\mu_n(s,t) := \frac{1}{n} \# \{ 1 \leq k \leq n: \hbox{Re}(\lambda_k) \leq s; \hbox{Im}(\lambda_k) \leq t \}$

then with probability 1, $\mu_n$ should converge uniformly to the uniform distribution $\mu_\infty$ of the unit circle, defined as

$\mu_\infty(s,t) := \frac{1}{\pi} \hbox{mes}(\{ (x,y): |x|^2 + |y|^2 \leq 1; x \leq s; y \leq t \}.$

This statement is known as the circular law conjecture. In the case when x is a complex Gaussian, this law was verified by Mehta (using an explicit formula of Ginibre for the joint density function of the eigenvalues in this case). A strategy for attacking the general case was then formulated by Girko, although a fully rigorous execution of that strategy was first achieved by Bai (and then improved slightly by Bai and Silverstein). They established the circular law under the assumption that x had slightly better than bounded second moment (i.e. ${\Bbb E}|x|^{2+\delta} < \infty$ for some $\delta > 0$), but more importantly that the probability density function of x in the complex plane was bounded (in particular, this ruled out all discrete random variables, such as the Bernoulli random variable). The reason for this latter restriction was in order to control the event that the matrix $N_n$ (or more precisely $\frac{1}{\sqrt{n}} N_n - z I$ for various complex numbers z) becomes too ill-conditioned by having a very small least singular value.

In the last few years, work of Rudelson, of myself with Van Vu, and of Rudelson-Vershynin (building upon earlier work of Kahn, Komlos, and Szemerédi, and of Van and myself), have opened the way to control the condition number of random matrices even when the matrices are discrete, and so there have been a recent string of results using these techniques to extend the circular law to discrete settings. In particular, Gotze and Tikhomirov established the circular law for discrete random variables which were sub-Gaussian, which was then relaxed by Pan and Zhou to an assumption of bounded fourth moment. In our paper, we get very close to the ideal assumption of bounded second moment; we need ${\Bbb E} |x|^2 \log^C(1+|x|) < \infty$ for some $C > 16$. (The power of 16 in the logarithm can certainly be improved, though our methods do not allow the logarithm to be removed entirely.)

The main new difficulty that arises when relaxing the moment condition so close to the optimal one is that one begins to lose control on the largest singular value of $\frac{1}{\sqrt{n}} N_n$, i.e. on the operator norm of $\frac{1}{\sqrt{n}} N_n$. Under high moment assumptions (e.g. fourth moment) one can keep this operator norm bounded with reasonable probability (especially after truncating away some exceptionally large elements), but when the moment conditions are loosened, one can only bound this operator norm by a quantity bounded polynomially in n, even after truncation. This in turn causes certain metric entropy computations to become significantly more delicate, as one has to reduce the scale $\epsilon$ of the net below what one would ordinarily like to have.