Random matrix theory is now a big subject with applications in many disciplines of science, engineering and finance. This talk is a survey specifically oriented towards the needs and interests of a computationally inclined audience. We include the important mathematics (free probability) that permit the characterization of a large class of random matrices. We discuss how computational software is transforming this theory into practice by highlighting its use in the context of a stochastic eigen-inference application. / Singapore-MIT Alliance (SMA)

Cette thèse s'inscrit dans le domaine des matrices aléatoires et des techniques de grandes déviations. On s'attachera dans un premier temps à donner des inégalités de déviations pour différentes fonctionnelles du spectre qui reflètent leurs comportement de grandes déviations, pour des matrices de Wigner vérifiant une propriété de concentration indexée par un paramètre alpha ∈ (0,2]. Nous présenterons ensuite le principe de grandes déviations obtenu pour la plus grande valeur propre des matrices de Wigner sans queues Gaussiennes, dans la lignée du travail de Bordenave et Caputo, puis l'étude des grandes déviations des traces de matrices aléatoires que l'on aborde dans trois cas : le cas des beta-ensembles, celui des matrices de Wigner Gaussiennes, et enfin des matrices de Wigner sans queues Gaussiennes. Le cas Gaussien a été l'occasion de revisiter la preuve de Borell et Ledoux des grandes déviations des chaos de Wiener, que l'on prolonge en proposant un énoncé général de grandes déviations qui nous permet donner une autre preuve des principes de grandes déviations des matrices de Wigner sans queues Gaussiennes. Enfin, nous donnons une nouvelle preuve des grandes déviations de la mesure spectrale empirique des beta-ensembles associés à un potentiel quadratique, qui ne repose que sur leur représentation tridiagonale. / This thesis falls within the theory of random matrices and large deviations techniques. We mainly consider large deviations problems which involve a heavy-tail phenomenon. In a first phase, we will focus on finding concentration inequalities for different spectral functionals which reflect their large deviations behavior, for random Hermitian matrices satisfying a concentration property indexed by some alpha ∈ (0,2]. Then we will present the large deviations principle we obtained for the largest eigenvalue of Wigner matrices without Gaussian tails, in line with the work of Bordenave and Caputo. Another example of heavy-tail phenomenon is given by the large deviations of traces of random matrices which we investigate in three cases: the case of beta-ensembles, of Gaussian Wigner matrices, and the case of Wigner matrices without Gaussian tails. The Gaussian case was the opportunity to revisit Borell and Ledoux's proof of the large deviations of Wiener chaoses, which we investigate further by proposing a general large deviations statement, allowing us to give another proof of the large deviations principles known for the Wigner matrices without Gaussian tail. Finally, we give a new proof of the large deviations principles for the beta-ensembles with a quadratic potential, which relies only on the tridiagonal representation of these models. In particular, this result gives a proof of the large deviations of the GUE and GOE which does not rely on the knowledge of the law of the spectrum.

In the first part of this thesis, we study critical points of random polynomials. We choose two deterministic sequences of complex numbers, whose empirical measures converge to the same probability measure in complex plane. We make a sequence of polynomials whose zeros are chosen from either of sequences at random. We show that the limiting empirical measure of zeros and critical points agree for these polynomials. As a consequence we show that when we randomly perturb the zeros of a deterministic sequence of polynomials, the limiting empirical measures of zeros and critical points agree. This result can be interpreted as an extension of earlier results where randomness is reduced. Pemantle and Rivin initiated the study of critical points of random polynomials. Kabluchko proved the result considering the zeros to be i.i.d. random variables. In the second part we deal with the spectrum of products of Ginibre matrices. Exact eigenvalue density is known for a very few matrix ensembles. For the known ones they often lead to determinantal point process. Let X1, X2,..., Xk be i.i.d Ginibre matrices of size n ×n whose entries are standard complex Gaussian random variables. We derive eigenvalue density for matrices of the form X1 ε1 X2 ε2 ... Xk εk , where εi = ±1 for i =1,2,..., k. We show that the eigenvalues form a determinantal point process. The case where k =2, ε1 +ε2 =0 was derived earlier by Krishnapur. In the case where εi =1 for i =1,2,...,n was derived by Akemann and Burda. These two known cases can be obtained as special cases of our result.

Orientador: Dimitar Kolev Dimitrov / Banca: Carlos Tomei / Banca: José  Alberto Cuminato / Resumo: Neste trabalho estudaremos a relação existente entre polinômios ortogonais e matrizes aleatórias. Exibiremos uma caracterização de polinômios ortogonais via problemas de Riemann-Hilbert, a qual tem se mostrado uma ferramenta única para obtenção de assintóticas de polinômios ortogonais. Posteriormente, estudaremos a teoria básica dos ensembles unitários de matrizes aleatórias. Por fim, mostraremos como a teoria de assintóticas de polinômios ortogonais pode ser usada na análise assintótica de estatísticas de matrizes aleatórias, nos levando a resultados de universalidade para os ensembles unitários / Abstract: We will exhibit a characterization of orthogonal p olynomials via Riemann-Hilbert problems, which has been shown a powerful to ol for studying asymptotics of orthogonal polynomials. Posteriorly we will review the basic theory of unitary ensembles of random matrices. At the end, we will show how asymptotics of orthogonal polynomials can be used to study asymptotics of several statistics in random matrix theory, obtaining universality results for the unitary ensembles / Mestre

In this thesis, we study the exact eigenvalue distribution of product of independent rectangular complex Gaussian matrices and also that of product of independent truncated Haar unitary matrices and inverses of truncated Haar unitary matrices. The eigenvalues of these random matrices form determinantal point processes on the complex plane. We also study the limiting expected empirical distribution of appropriately scaled eigenvalues of those matrices as the size of matrices go to infinity. We give the first example of a random matrix whose eigenvalues form a non-rotation invariant determinantal point process on the plane. The second theme of this thesis is infinite products of random matrices. We study the asymptotic behaviour of singular values and absolute values of eigenvalues of product of i .i .d matrices of fixed size, as the number of matrices in the product in-creases to infinity. In the special case of isotropic random matrices, We derive the asymptotic joint probability density of the singular values and also that of the absolute values of eigenvalues of product of right isotropic random matrices and show them to be equal. As a corollary of these results, we show probability that all the eigenvalues of product of certain i .i .d real random matrices of fixed size converges to one, as the number of matrices in the product increases to infinity.

L'objectif principal de cette thèse est de répondre à la question: étant donné une matrice aléatoire avec spectre réel, combien de valeurs propres tomber entre A et B? Ceci est une question fondamentale dans la théorie des matrices aléatoires et toutes ses applications, autant de problèmes peuvent être traduits en comptant les valeurs propres à l'intérieur des régions du spectre. Nous appliquons la méthode de gaz Coulomb à ce problème général dans le cadre de différents ensembles de matrice aléatoire et l'on obtient de résultats pour intervalles générales [a, b]. Ces résultats sont particulièrement intéressants dans l'étude des variations des systèmes fermioniques unidimensionnelles de particules confinées non-interaction à la température zéro. / The main goal of this thesis is to answer the question: given a random matrix with real spectrum, how many eigenvalues fall between a and b? This is a fundamental question in random matrix theory and all of its applications, as many problems can be translated into counting eigenvalues inside regions of the spectrum. We apply the Coulomb gas method to this general problem in the context of different random matrix ensembles and we obtain many results for general intervals [a,b]. These results are particularly interesting in the study of fermionic fluctuations for one-dimensional systems of confined non-interacting particles at zero temperature.

De nos jours, il est de plus en plus fréquent de travailler sur des bases de données de très grandes tailles dans plein de domaines différents. Cela ouvre la voie à de nouvelles possibilités d'exploitation ou d'exploration de l'information, et de nombreuses technologies numériques ont été créées récemment dans cette optique. D'un point de vue théorique, ce problème nous contraint à revoir notre manière d'analyser et de comprendre les données enregistrées. En effet, dans cet univers communément appelé « Big Data », un bon nombre de méthodes traditionnelles d'inférence statistique multivariée deviennent inadaptées. Le but de cette thèse est donc de mieux comprendre ce phénomène, appelé fléau (ou malédiction) de la dimension, et ensuite de proposer différents outils statistiques exploitant explicitement la dimension du problème et permettant d'extraire des informations fiables des données. Pour cela, nous nous intéresserons beaucoup aux vecteurs propres de matrices symétriques. Nous verrons qu’il est possible d’extraire de l'information présentant un certain degré d’universalité. En particulier, cela nous permettra de construire des estimateurs optimaux, observables, et cohérents avec le régime de grande dimension. / Nowadays, it is easy to get a lot ofquantitative or qualitative data in a lot ofdifferent fields. This access to new databrought new challenges about data processingand there are now many different numericaltools to exploit very large database. In atheoretical standpoint, this framework appealsfor new or refined results to deal with thisamount of data. Indeed, it appears that mostresults of classical multivariate statisticsbecome inaccurate in this era of “Big Data”.The aim of this thesis is twofold: the first one isto understand theoretically this so-called curseof dimensionality that describes phenomenawhich arise in high-dimensional space.Then, we shall see how we can use these toolsto extract signals that are consistent with thedimension of the problem. We shall study thestatistics of the eigenvalues and especially theeigenvectors of large symmetrical matrices. Wewill highlight that we can extract someuniversal properties of these eigenvectors andthat will help us to construct estimators that areoptimal, observable and consistent with thehigh dimensional framework.

A k × n array with entries from a q-letter alphabet is called a t-covering array if each t × n submatrix contains amongst its columns each one of the gt different words of length t that can be produced by the q letters. In the present article we use a probabilistic approach based on an appropriate Markov chain embedding technique, to study a t-covering problem where, instead of looking at all possible t ×n submatrices, we consider only submatrices of dimension t ×n with its rows being consecutive rows of the original k × n array. Moreover, an exact formula is established for the probability distribution function of the random variable, which enumerates the number of deficient submatrices (i.e., submatrices with at least one missing word, amongst their columns), in the case of a k × n binary matrix (q = 2) obtained by realizing kn Bernoulli variables.

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[pt] Nessa dissertação vamos abordar a famosa lei do Semicírculo de Wigner, que dá uma descrição do comportamento do espectro de autovalores de matrizes aleatórias simétricas. A demonstração combina ideias e técnicas de Combinatória e Probabilidade, incluindo uma analise cautelosa dos momentos da distribuição de autovalores. / [en] In this dissertation we will approach the famous Wigner s Semicircle Law, which gives a description of the behavior of the eigenvalue spectrum of symmetric random matrices. The proof combines ideas and techniques from Combinatorics and Probability, including a careful analysis of the moments of the eigenvalue distribution.