The numerical solution of sparse matrix equations by fast methods and associated computational techniques

The numerical solution of sparse matrix equations by fast methods and associated computational technique

The singular values of the GUE (less is more)

Some properties that nominally involve the eigenvalues of Gaussian Unitary Ensemble (GUE) can instead be phrased in terms of singular values. By discarding the signs of the eigenvalues, we gain access to a surprising decomposition: the singular values of the GUE are distributed as the union of the singular values of two independent ensembles of Laguerre type. This independence is remarkable given the well known phenomenon of eigenvalue repulsion. The structure of this decomposition reveals that several existing observations about large n limits of the GUE are in fact manifestations of phenomena that are already present for finite random matrices. We relate the semicircle law to the quarter-circle law by connecting Hermite polynomials to generalized Laguerre polynomials with parameter ± 1/2. Similarly, we write the absolute value of the determinant of the n x n GUE as a product n independent random variables to gain new insight into its asymptotic log-normality. The decomposition also provides a description of the distribution of the smallest singular value of the GUE, which in turn permits the study of the leading order behavior of the condition number of GUE matrices. The study is motivated by questions involving the enumeration of orientable maps, and is related to questions involving powers of complex Ginibre matrices. The inescapable conclusion of this work is that the singular values of the GUE play an unpredictably important role that had gone unnoticed for decades even though, in hindsight, so many clues had been around.National Science Foundation (U.S.) (Grant DMS–1035400)National Science Foundation (U.S.) (Grant DMS–1016125

Rational Krylov subspace methods for phi-functions in exponential integrators

Exponential integrators are a class of numerical methods for stiff systems of differential equations which require the computation of products of so-called matrix phi-functions and vectors. In this thesis, we consider the approximation of these matrix functions times some vector by rational and extended Krylov subspace methods. For arbitrary matrices with a field of values in the left complex half-plane, a uniform approximation is obtained that predicts a sublinear convergence

Schur Averages in Random Matrix Ensembles

The main focus of this PhD thesis is the study of minors of Toeplitz, Hankel and Toeplitz±Hankel matrices. These can be expressed as matrix models over the classical Lie groups G(N) = U(N); Sp(2N);O(2N);O(2N + 1), with the insertion of irreducible characters associated to each of the groups. In order to approach this topic, we consider matrices generated by formal power series in terms of symmetric functions. We exploit these connections to obtain several relations between the models over the different groups G(N), and to investigate some of their structural properties. We compute explicitly several objects of interest, including a variety of matrix models, evaluations of certain skew Schur polynomials, partition functions and Wilson loops of G(N) Chern-Simons theory on S3, and fermion quantum models with matrix degrees of freedom. We also explore the connection with orthogonal polynomials, and study the large N behaviour of the average of a characteristic polynomial in the Laguerre Unitary Ensemble by means of the associated Riemann-Hilbert problem. We gratefully acknowledge the support of the Fundação para a Ciência e a Tecnologia through its LisMath scholarship PD/BD/113627/2015, which made this work possible