Pseudospectral Shattering, the Sign Function, and Diagonalization in Nearly Matrix Multiplication Time

We exhibit a randomized algorithm which given a square $n\times n$ complex matrix $A$ with $\|A\| \le 1$ and $\delta>0$, computes with high probability invertible $V$ and diagonal $D$ such that $$\|A-VDV^{-1}\|\le \delta$$ and $\|V\|\|V^{-1}\| \le O(n^{2.5}/\delta)$ in $O(T_{MM}\>(n)\log^2(n/\delta))$ arithmetic operations on a floating point machine with $O(\log^4(n/\delta)\log n)$ bits of precision. Here $T_{MM}\>(n)$ is the number of arithmetic operations required to multiply two $n\times n$ complex matrices numerically stably, with $T_{MM}\,\,(n)=O(n^{\omega+\eta}\>\>)$ for every $\eta>0$, where $\omega$ is the exponent of matrix multiplication. The algorithm is a variant of the spectral bisection algorithm in numerical linear algebra (Beavers and Denman, 1974). This running time is optimal up to polylogarithmic factors, in the sense that verifying that a given similarity diagonalizes a matrix requires at least matrix multiplication time. It significantly improves best previously provable running times of $O(n^{10}/\delta^2)$ arithmetic operations for diagonalization of general matrices (Armentano et al., 2018), and (w.r.t. dependence on $n$) $O(n^3)$ arithmetic operations for Hermitian matrices (Parlett, 1998). The proof rests on two new ingredients. (1) We show that adding a small complex Gaussian perturbation to any matrix splits its pseudospectrum into $n$ small well-separated components. This implies that the eigenvalues of the perturbation have a large minimum gap, a property of independent interest in random matrix theory. (2) We rigorously analyze Roberts' Newton iteration method for computing the matrix sign function in finite arithmetic, itself an open problem in numerical analysis since at least 1986. This is achieved by controlling the evolution the iterates' pseudospectra using a carefully chosen sequence of shrinking contour integrals in the complex plane.Comment: 78 pages, 3 figures, comments welcome. Slightly edited intro from previous version + explicit statement of forward error Theorem (Corolary 1.7). Minor corrections mad

Semi-classical Analysis and Pseudospectra

We prove an approximate spectral theorem for non-self-adjoint operators and investigate its applications to second order differential operators in the semi-classical limit. This leads to the construction of a twisted FBI transform. We also investigate the connections between pseudospectra and boundary conditions in the semi-classical limit

Mean left-right eigenvector self-overlap in the real Ginibre ensemble

We study analytically the Chalker-Mehlig mean diagonal overlap $\mathcal{O}(z)$ between left and right eigenvectors associated with a complex eigenvalue $z$ of $N\times N$ matrices in the real Ginibre ensemble (GinOE). We first derive a general finite $N$ expression for the mean overlap and then investigate several scaling regimes in the limit $N\rightarrow \infty$. While in the generic spectral bulk and edge of the GinOE the limiting expressions for $\mathcal{O}(z)$ are found to coincide with the known results for the complex Ginibre ensemble (GinUE), in the region of eigenvalue depletion close to the real axis the asymptotic for the GinOE is considerably different. We also study numerically the distribution of diagonal overlaps and conjecture that it is the same in the bulk and at the edge of both the GinOE and GinUE, but essentially different in the depletion region of the GinOE.Comment: 23 pages, 7 figure

Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization

We present a randomized, inverse-free algorithm for producing an approximate diagonalization of any $n \times n$ matrix pencil $(A,B)$. The bulk of the algorithm rests on a randomized divide-and-conquer eigensolver for the generalized eigenvalue problem originally proposed by Ballard, Demmel, and Dumitriu [Technical Report 2010]. We demonstrate that this divide-and-conquer approach can be formulated to succeed with high probability as long as the input pencil is sufficiently well-behaved, which is accomplished by generalizing the recent pseudospectral shattering work of Banks, Garza-Vargas, Kulkarni, and Srivastava [Foundations of Computational Mathematics 2022]. In particular, we show that perturbing and scaling $(A,B)$ regularizes its pseudospectra, allowing divide-and-conquer to run over a simple random grid and in turn producing an accurate diagonalization of $(A,B)$ in the backward error sense. The main result of the paper states the existence of a randomized algorithm that with high probability (and in exact arithmetic) produces invertible $S,T$ and diagonal $D$ such that $||A - SDT^{-1}||_2 \leq \varepsilon$ and $||B - SIT^{-1}||_2 \leq \varepsilon$ in at most $O \left( \log(n) \log^2 \left( \frac{n}{\varepsilon} \right) T_{\text{MM}}(n) \right)$ operations, where $T_{\text{MM}}(n)$ is the asymptotic complexity of matrix multiplication. This not only provides a new set of guarantees for highly parallel generalized eigenvalue solvers but also establishes nearly matrix multiplication time as an upper bound on the complexity of exact arithmetic matrix pencil diagonalization.Comment: 58 pages, 8 figures, 2 table

Propriétés spectrales des opérateurs non-auto-adjoints aléatoires

In this thesis we are interested in the spectral properties of random non-self-adjoint operators. Weare going to consider primarily the case of small random perturbations of the following two types of operators: 1. a class of non-self-adjoint h-differential operators Ph, introduced by M. Hager [32], in the semiclassical limit (h→0); 2. large Jordan block matrices as the dimension of the matrix gets large (N→∞). In case 1 we are going to consider the operator Ph subject to small Gaussian random perturbations. We let the perturbation coupling constant δ be e (-1/Ch) ≤ δ ⩽ h(k), for constants C, k > 0 suitably large. Let ∑ be the closure of the range of the principal symbol. Previous results on the same model by M. Hager [32], W. Bordeaux-Montrieux [4] and J. Sjöstrand [67] show that if δ ⪢ e(-1/Ch) there is, with a probability close to 1, a Weyl law for the eigenvalues in the interior of the pseudospectrumup to a distance ⪢ (-h ln δ h) 2/3 to the boundary of ∑. We will study the one- and two-point intensity measure of the random point process of eigenvalues of the randomly perturbed operator and prove h-asymptotic formulae for the respective Lebesgue densities describing the one- and two-point behavior of the eigenvalues in ∑. Using the density of the one-point intensity measure, we will give a complete description of the average eigenvalue density in ∑ describing as well the behavior of the eigenvalues at the pseudospectral boundary. We will show that there are three distinct regions of different spectral behavior in ∑. The interior of the of the pseudospectrum is solely governed by a Weyl law, close to its boundary there is a strong spectral accumulation given by a tunneling effect followed by a region where the density decays rapidly. Using the h-asymptotic formula for density of the two-point intensity measure we will show that two eigenvalues of randomly perturbed operator in the interior of ∑ exhibit close range repulsion and long range decoupling. In case 2 we will consider large Jordan block matrices subject to small Gaussian random perturbations. A result by E.B. Davies and M. Hager [16] shows that as the dimension of the matrix gets large, with probability close to 1, most of the eigenvalues are close to a circle. They, however, only state a logarithmic upper bound on the number of eigenvalues in the interior of that circle. We study the expected eigenvalue density of the perturbed Jordan block in the interior of thatcircle and give a precise asymptotic description. Furthermore, we show that the leading contribution of the density is given by the Lebesgue density of the volume form induced by the Poincarémetric on the disc D(0, 1).Dans cette thèse, nous nous intéressons aux propriétés spectrales des opérateurs non-auto-adjoints aléatoires. Nous allons considérer principalement les cas des petites perturbations aléatoires de deux types des opérateurs non-auto-adjoints suivants :1. une classe d’opérateurs non-auto-adjoints h-différentiels Ph, introduite par M. Hager [32],dans la limite semiclassique (h→0); 2. des grandes matrices de Jordan quand la dimension devient grande (N→∞). Dans le premier cas nous considérons l’opérateur Ph soumis à de petites perturbations aléatoires. De plus, nous imposons que la constante de couplage δ vérifie e (-1/Ch) ≤ δ ⩽ h(k), pour certaines constantes C, k > 0 choisies assez grandes. Soit ∑ l’adhérence de l’image du symbole principal de Ph. De précédents résultats par M. Hager [32], W. Bordeaux-Montrieux [4] et J. Sjöstrand [67] montrent que, pour le même opérateur, si l’on choisit δ ⪢ e(-1/Ch), alors la distribution des valeurs propres est donnée par une loi de Weyl jusqu’à une distance ⪢ (-h ln δ h) 2/3 du bord de ∑. Nous étudions la mesure d’intensité à un et à deux points de la mesure de comptage aléatoire des valeurs propres de l’opérateur perturbé. En outre, nous démontrons des formules h-asymptotiques pour les densités par rapport à la mesure de Lebesgue de ces mesures qui décrivent le comportement d’un seul et de deux points du spectre dans ∑. En étudiant la densité de la mesure d’intensité à un point, nous prouvons qu’il y a une loi de Weyl à l’intérieur du pseudospectre,une zone d’accumulation des valeurs propres dûe à un effet tunnel près du bord du pseudospectre suivi par une zone où la densité décroît rapidement. En étudiant la densité de la mesure d’intensité à deux points, nous prouvons que deux valeurs propres sont répulsives à distance courte et indépendantes à grande distance à l’intérieur de ∑. Dans le deuxième cas, nous considérons des grands blocs de Jordan soumis à des petites perturbations aléatoires gaussiennes. Un résultat de E.B. Davies et M. Hager [16] montre que lorsque la dimension de la matrice devient grande, alors avec probabilité proche de 1, la plupart des valeurs propres sont proches d’un cercle. De plus, ils donnent une majoration logarithmique du nombre de valeurs propres à l’intérieur de ce cercle. Nous étudions la répartition moyenne des valeurs propres à l’intérieur de ce cercle et nous en donnons une description asymptotique précise. En outre, nous démontrons que le terme principal de la densité est donné par la densité par rapport à la mesure de Lebesgue de la forme volume induite par la métrique de Poincaré sur la disque D(0, 1)