Showing papers on "Spectrum of a matrix published in 2015"

Distribution of complex transmission eigenvalues for spherically stratified media

Abstract: In this paper, we employ transformation operators and Levinson's density formula to study the distribution of interior transmission eigenvalues for a spherically stratified media. In particular, we show that under smoothness condition on the index of refraction that there exist an infinite number of complex eigenvalues and there exist situations when there are no real eigenvalues. We also consider the case when absorption is present and show that under appropriate conditions there exist an infinite number of eigenvalues near the real axis.

Viewing the Steklov eigenvalues of the Laplace operator as critical Neumann eigenvalues

Abstract: We consider the Steklov eigenvalues of the Laplace operator as limiting Neumann eigenvalues in a problem of boundary mass concentration. We discuss the asymptotic behavior of the Neumann eigenvalues in a ball and we deduce that the Steklov eigenvalues minimize the Neumann eigenvalues. Moreover, we study the dependence of the eigenvalues of the Steklov problem upon perturbation of the mass density and show that the Steklov eigenvalues violates a maximum principle in spectral optimization problems.

The role eigenvalues play in forming GMRES residual norms with non-normal matrices

Abstract: In this paper we give explicit expressions for the norms of the residual vectors generated by the GMRES algorithm applied to a non-normal matrix. They involve the right-hand side of the linear system, the eigenvalues, the eigenvectors and, in the non-diagonalizable case, the principal vectors. They give a complete description of how eigenvalues contribute in forming residual norms and offer insight in what quantities can prevent GMRES from being governed by eigenvalues.

Spectra of weighted scale-free networks.

Abstract: Much information about the structure and dynamics of a network is encoded in the eigenvalues of its transition matrix. In this paper, we present a first study on the transition matrix of a family of weight driven networks, whose degree, strength, and edge weight obey power-law distributions, as observed in diverse real networks. We analytically obtain all the eigenvalues, as well as their multiplicities. We then apply the obtained eigenvalues to derive a closed-form expression for the random target access time for biased random walks occurring on the studied weighted networks. Moreover, using the connection between the eigenvalues of the transition matrix of a network and its weighted spanning trees, we validate the obtained eigenvalues and their multiplicities. We show that the power-law weight distribution has a strong effect on the behavior of random walks.

Spectrum of walk matrix for Koch network and its application.

Abstract: Various structural and dynamical properties of a network are encoded in the eigenvalues of walk matrix describing random walks on the network. In this paper, we study the spectra of walk matrix of the Koch network, which displays the prominent scale-free and small-world features. Utilizing the particular architecture of the network, we obtain all the eigenvalues and their corresponding multiplicities. Based on the link between the eigenvalues of walk matrix and random target access time defined as the expected time for a walker going from an arbitrary node to another one selected randomly according to the steady-state distribution, we then derive an explicit solution to the random target access time for random walks on the Koch network. Finally, we corroborate our computation for the eigenvalues by enumerating spanning trees in the Koch network, using the connection governing eigenvalues and spanning trees, where a spanning tree of a network is a subgraph of the network, that is, a tree containing all the nodes.

Eigenvalues for the transition matrix of a small-world scale-free network: Explicit expressions and applications

Abstract: The eigenvalues of the transition matrix for random walks on a network play a significant role in the structural and dynamical aspects of the network. Nevertheless, it is still not well understood how the eigenvalues behave in small-world and scale-free networks, which describe a large variety of real systems. In this paper, we study the eigenvalues for the transition matrix of a network that is simultaneously scale-free, small-world, and clustered. We derive explicit simple expressions for all eigenvalues and their multiplicities, with the spectral density exhibiting a power-law form. We then apply the obtained eigenvalues to determine the mixing time and random target access time for random walks, both of which exhibit unusual behaviors compared with those for other networks, signaling discernible effects of topologies on spectral features. Finally, we use the eigenvalues to count spanning trees in the network.

Design of a New Concentration Series for the Orthogonal Sample Design Approach and Estimation of the Number of Reactions in Chemical Systems

Abstract: A new concentration series is proposed for the construction of a two-dimensional (2D) synchronous spectrum for orthogonal sample design analysis to probe intermolecular interaction between solutes dissolved in the same solutions. The obtained 20 synchronous spectrum possesses the following two properties: (1) cross peaks in the 20 synchronous spectra can be used to reflect intermolecular interaction reliably, since interference portions that have nothing to do with intermolecular interaction are completely removed, and (2) the two-dimensional synchronous spectrum produced can effectively avoid accidental collinearity. Hence, the correct number of nonzero eigenvalues can be obtained so that the number of chemical reactions can be estimated. In a real chemical system, noise present in one-dimensional spectra may also produce nonzero eigenvalues. To get the correct number of chemical reactions, we classified nonzero eigenvalues into significant nonzero eigenvalues and insignificant nonzero eigenvalues. Significant nonzero eigenvalues can be identified by inspecting the pattern of the corresponding eigenvector with help of the Durbin-Watson statistic. As a result, the correct number of chemical reactions can be obtained from significant nonzero eigenvalues. This approach provides a solid basis to obtain insight into subtle spectral variations caused by intermolecular interaction.

Inverse problem with transmission eigenvalues for the discrete Schrödinger equation

Abstract: The discrete Schrodinger equation with the Dirichlet boundary condition is considered on a half-line lattice when the potential is real valued and compactly supported. The inverse problem of recovery of the potential from the corresponding transmission eigenvalues is analyzed. The Marchenko method and the Gel’fand-Levitan method are used to solve the inverse problem uniquely, except in one “unusual” case where the sum of the transmission eigenvalues is equal to a certain integer related to the support of the potential. It is shown that in the unusual case, there may be a unique potential corresponding to a given set of transmission eigenvalues, there may be a finite number of distinct potentials for a given set of transmission eigenvalues, or there may be infinitely many potentials for a given set of transmission eigenvalues. The theory presented is illustrated with several explicit examples.

From gap probabilities in random matrix theory to eigenvalue expansions

Abstract: We present a method to derive asymptotics of eigenvalues for trace-class integral operators $K:L^2(J;d\lambda)\circlearrowleft$, acting on a single interval $J\subset\mathbb{R}$, which belong to the ring of integrable operators \cite{IIKS}. Our emphasis lies on the behavior of the spectrum $\{\lambda_i(J)\}_{i=0}^{\infty}$ of $K$ as $|J|\rightarrow\infty$ and $i$ is fixed. We show that this behavior is intimately linked to the analysis of the Fredholm determinant $\det(I-\gamma K)|_{L^2(J)}$ as $|J|\rightarrow\infty$ and $\gamma\uparrow 1$ in a Stokes type scaling regime. Concrete asymptotic formulae\, are obtained for the eigenvalues of Airy and Bessel kernels in random matrix theory.

Spectral bounds for matrix polynomials with unitary coefficients

Abstract: It is well known that the eigenvalues of any unitary matrix lie on the unit circle. The purpose of this paper is to prove that the eigenvalues of any matrix polynomial, with unitary coefficients, lie inside the annulus A_{1/2,2) := {z ∈ C | 1/2 < |z| < 2}. The foundations of this result rely on an operator version of Rouche’s theorem and the intermediate value theorem.

On the eigenvalues of a 2 2 block operator matrix

Abstract: A \(2\times2\) block operator matrix \({\mathbf H}\) acting in the direct sum of one- and two-particle subspaces of a Fock space is considered. The existence of infinitely many negative eigenvalues of \(H_{22}\) (the second diagonal entry of \({\bf H}\)) is proved for the case where both of the associated Friedrichs models have a zero energy resonance. For the number \(N(z)\) of eigenvalues of \(H_{22}\) lying below \(z\lt0\), the following asymptotics is found \[\lim\limits_{z\to -0} N(z) |\log|z||^{-1}=\,{\mathcal U}_0 \quad (0\lt {\mathcal U}_0\lt \infty).\] Under some natural conditions the infiniteness of the number of eigenvalues located respectively inside, in the gap, and below the bottom of the essential spectrum of \({\mathbf H}\) is proved.

Pseudospectra of isospectrally reduced matrices

Abstract: SUMMARY An isospectral matrix reduction is a procedure that reduces the size of a matrix while maintaining its eigenvalues up to a known set. As to not violate the fundamental theorem of algebra, the reduced matrices have rational functions as entries. Because isospectral reductions can preserve the spectrum of a matrix, they are fundamentally different from say the restriction of a matrix to an invariant subspace. We show that the notion of pseudospectrum can be extended to a wide class of matrices with rational function entries and that the pseudospectrum of such matrices shrinks with isospectral reductions. Hence, the eigenvalues of a reduced matrix are more robust to entry-wise perturbations than the eigenvalues of the original matrix. Moreover, the isospectral reductions considered here are more general than those considered elsewhere. We also introduce the notion of an inverse pseudospectrum (or pseudoresonances), which indicates how stable the poles of a rational function valued matrix are to entry-wise perturbations. Illustrations of these concepts are given for mass-spring networks. Copyright © 2014 John Wiley & Sons, Ltd.

A Contour-integral Based Method for Counting the Eigenvalues Inside a Region in the Complex Plane

Abstract: In many applications, the information about the number of eigenvalues inside a given region is required. In this paper, we propose a contour-integral based method for this purpose. The new method is motivated by two findings. There exist methods for estimating the number of eigenvalues inside a region in the complex plane. But our method is able to compute the number of eigenvalues inside the given region exactly. An appealing feature of our method is that it can integrate with the recently developed contour-integral based eigensolvers to help them detect whether all desired eigenvalues are found. Numerical experiments are reported to show the viability of our new method.

Spectra of nearly Hermitian random matrices

Abstract: We consider the eigenvalues and eigenvectors of matrices of the form M + P, where M is an n by n Wigner random matrix and P is an arbitrary n by n deterministic matrix with low rank. In general, we show that none of the eigenvalues of M + P need be real, even when P has rank one. We also show that, except for a few outlier eigenvalues, most of the eigenvalues of M + P are within 1/n of the real line, up to small order corrections. We also prove a new result quantifying the outlier eigenvalues for multiplicative perturbations of the form S ( I + P ), where S is a sample covariance matrix and I is the identity matrix. We extend our result showing all eigenvalues except the outliers are close to the real line to this case as well. As an application, we study the critical points of the characteristic polynomials of nearly Hermitian random matrices.

Discrete spectrum of a noncompact perturbation of a three-particle Schrödinger operator on a lattice

Abstract: We consider a system of three arbitrary quantum particles on a three-dimensional lattice interacting via attractive pair-contact potentials and attractive potentials of particles at the nearest-neighbor sites. We prove that the Hamiltonian of the corresponding three-particle system has infinitely many eigenvalues. We also list different types of attractive potentials whose eigenvalues can be to the left of the essential spectrum, in a gap in the essential spectrum, and in the essential spectrum of the considered operator.

An Empirical Method for Estimating the Number of Signal Sources

Abstract: In this paper we propose an empirical method for estimating the number of sources of signals impinging on multiple sensors. The method is based on the analysis of the norms of vectors whose elements are the normalized eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. It is shown that such norms can be used to classify the eigenvalues in two groups: the largest and the remaining ones, thus allowing for the estimation of the number of sources without the knowledge of any additional parameter. It is shown that, in some situations, our norm-based method produces satisfactory performance when compared to a recently proposed random matrix theory method

First‐order systems in C2 on R with periodic matrix potentials and vanishing instability intervals

Abstract: First-order systems in on with absolutely continuous real symmetric π-periodic matrix potentials are considered A thorough analysis of the discriminant is given Interlacing of the eigenvalues of the periodic, antiperiodic and Dirichlet-type boundary value problems on [0,π] is shown for a suitable indexing of the eigenvalues The periodic and antiperiodic eigenvalues are characterized in terms of Dirichlet-type eigenvalues It is shown that all instability intervals vanish if and only if the potential is the product of an absolutely continuous real scalar valued function with the identity matrix Copyright © 2014 John Wiley & Sons, Ltd

Generalization of Samuelson’s inequality and location of eigenvalues

Abstract: We prove a generalization of Samuelson’s inequality for higher order central moments. Bounds for the eigenvalues are obtained when a given complex n × n matrix has real eigenvalues. Likewise, we discuss bounds for the roots of polynomial equations.

The existence and location of eigenvalues of the one particle discrete Schroedinger operators

Abstract: We consider a quantum particle moving in the one dimensional lattice Z and interacting with a indefinite sign external field v. We prove that the associated discrete Schroedinger operator H can have one or two eigenvalues, situated as below the bottom of the essential spectrum, as well as above its top. Moreover, we show that the operator H can have two eigenvalues outside of the essential spectrum such that one of them is situated below the bottom of the essential spectrum, and other one above its top.

On eigenvalue inequalities of the Newton potential

Abstract: In this paper we compare the eigenvalues of the Newton potential with the Dirichlet eigenvalues and the Neumann eigenvalues in a bounded domain in Rd. We also prove Rayleigh-Faber-Krahn inequality for the Laplacian with a non-local type boundary condition.

A new Gershgorin‐type result for the localisation of the spectrum of matrices

Abstract: We present a Gershgorin's type result on the localisation of the spectrum of a matrix. Our method is elementary as it relies upon the method of Schur complements, but it outperforms the one based on the Cassini ovals of Ostrovski and Brauer. Furthermore, it yields estimates that hold without major differences in the cases of both scalar and operator matrices. Several refinements of known results are obtained.

Tropical methods for the localization of eigenvalues and application to their numerical computation

Abstract: In this thesis we use tropical mathematics to locate and numerically compute eigenvalues of matrices and matrix polynomials. The first part of the work focuses on eigenvalues of matrices, while the second part focuses on matrix polynomials and adds a numerical experimental side along the theoretical one. By “locating” an eigenvalue we mean being able to identify some bounds within which it must lie. This can be useful in situations where one only needs approximate eigenvalues; moreover, they make good starting values for iterative eigenvalue-finding algorithms. Rather than full location, our result for matrices is in the form of majorization bounds to control the absolute value of the eigenvalues. These bounds are to some extent a generalization to matrices of a result proved by Ostrowski for polynomials: he showed (albeit with different terminology) that the product of the k largest absolute values of the roots of a polynomial can be bounded from above and below by the product of its k largest tropical (max-times) roots, up to multiplicative factors which are independent of the coefficients of the polynomial. We prove an analogous result for matrices: the product of the k largest absolute values of eigenvalues is bounded, up to a multiplicative factor, by the product of the k largest tropical eigenvalues. It should be noted that tropical eigenvalues can be computed by using the solution to a parametric optimal assignment problem, in a way that is robust with respect to small perturbations in the data. Another thing worth mentioning is that the multiplicative factor in the bound is of combinatorial nature and it is reminiscent of a work by Friedland, who essentially proved a specialization of our result to the particular case k = 1 (i.e. for the largest eigenvalue only). We can interpret the absolute value as an archimedean valuation; in this light, there is a correspondence between the present result and previous work by Akian, Bapat and Gaubert, who dealt with the same problem for matrices over fields with non- archimedean valuation (specifically Puiseux series, with the leading exponent as valuation) and showed in that case more stringent bounds, with no multiplicative factor, and with generic equality rather than upper and lower bounds. The second part of the thesis revolves around the computation of eigenvalues of matrix polynomials. For linear matrix polynomials, stable algorithms such as the QZ method have been known for a long time. Eigenproblems for matrix polynomials of higher degree are usually reduced to the linear case, using a linearization such as the companion linearization. This however can worsen the condition number and backward error of the computed eigenvalue with respect to perturbations in the coefficients of the original polynomial (even if they remain stable in the coefficients of the linearized). To mitigate this inconvenience it is common to perform a scaling of the matrix polynomial before linearizing. Various scaling methods have been proposed. In our work, we introduce a two-sided diagonal scaling strategy based on the tropical eigenvalues of the matrix polynomial obtained by taking entrywise valuation of the original one (and we will consider both the archimedean and non-archimedean case). We study the effect of this scaling on the conditioning and backward error, with both analytic formulas and numerical examples, showing that it can increase the accuracy of the computed eigenvalues by several orders of magnitude.

A Subspace Iteration for Calculating a Cluster of Exterior Eigenvalues

Abstract: In this paper we present a new subspace iteration for calculating eigenvalues of symmetric matrices. The method is designed to compute a cluster of k exterior eigenvalues. For example, k eigenvalues with the largest absolute values, the k algebraically largest eigenvalues, or the k algebraically smallest eigenvalues. The new iteration applies a Restarted Krylov method to collect information on the desired cluster. It is shown that the estimated eigenvalues proceed monotonically toward their limits. Another innovation regards the choice of starting points for the Krylov subspaces, which leads to fast rate of convergence. Numerical experiments illustrate the viability of the proposed ideas.

Implementing Approximations to Extreme Eigenvalues and Eigenvalues of Irregular Surface Partitionings for Use in SAR and CAR Models

Abstract: Good approximations of eigenvalues exist for the regular square and hexagonal tessellations. To complement this situation, spatial scientists need good approximations of eigenvalues for irregular tessellations. Starting from known or approximated extreme eigenvalues, the remaining eigenvalues may be in turn approximated. One reason spatial scientists are interested in eigenvalues is because they are needed to calculate the Jacobian term in the autonormal probability model. If eigenvalues are not needed for model fitting, good approximations are needed to give the interval within which the spatial parameter will lie.

Graphs with Eigenvalues of High Multiplicity

Abstract: OF THE DISSERTATION Graphs with Eigenvalues of High Multiplicity

On eigenvalue accumulation for non-self-adjoint magnetic operators

Abstract: In this work, we use regularized determinant approach to study the discrete spectrum generated by relatively compact non-self-adjoint perturbations of the magnetic Schrodinger operator $(-i abla - \textbf{\textup{A}})^{2} - b$ in dimension $3$ with constant magnetic field of strength $b>0$. The situation near the Landau levels $2bq$, $q \in \mathbb{N}$, is more interesting since they play the role of thresholds of the spectrum of the free operator. First, we obtain sharp upper bounds on the number of the complex eigenvalues near the Landau levels. Under appropriate hypothesis, we then prove the presence of an infinite number of complex eigenvalues near each Landau level $2bq$, $q \in \mathbb{N}$, and the existence of sectors free of complex eigenvalues. We also prove that the eigenvalues are localized in certain sectors adjoining the Landau levels. In particular, we provide an adequate answer to the open problem from [34] about the existence of complex eigenvalues accumulating near the Landau levels. Furthermore, we prove that the Landau levels are the only possible accumulation points of the complex eigenvalues.

Extended Eigenvalues of Direct Sum of Operators

Abstract: In this paper a connection between extended eigenvalues of direct sum of operators in the direct sum of Hilbert spaces and their coordinate operators has been investigated. Moreover, the structure of the set of extended eigenvalues of normal compact operators has been researched.