We consider a quantum system whose unperturbed form consists of a self-adjoint Δ-operator on a 2-dimensional compact Riemannian manifold, which may or may not have a boundary. Then as a perturbation, we add a delta potential/point scatterer at some select point ρ. The perturbed self-adjoint operator is constructed rigorously by means of self-adjoint extension theory. We also consider a corresponding classical dynamical system on the cotangent/cosphere bundle, consisting of geodesic flow on the manifold, with specular reflection if there is a boundary. Chapter 2 describes the mathematics of the unperturbed and perturbed quantum systems, as well as outlining the classical dynamical system. Included in the discussion on the delta-perturbed quantum system is consideration concerning the strength of the delta potential. It is reckoned that the delta potential effectively has negative infinitesimal strength. Chapter 3 continues on with investigations from [KMW10], concerned with perturbed eigenfunctions that approximate to a linear combination of only two "surrounding" unperturbed eigenfunctions. In Thm. 4.4 of [KMW10], conditions are derived under which a sequence of perturbed eigenfunctions exhibits this behaviour in the limit. The approximating pair linear combinations belong to a class of quasimodes constructed within [KMW10]. The aim of Chapter 3 in this thesis is to improve on the result in [KMW10]. In Chapter 3, preliminary results are first derived constituting a broad consideration of the question of when a perturbed eigenfunction subsequence approaches linear combinations of only two surrounding unperturbed eigenfunctions. Afterwards, the central result of this Chapter, namely Thm. 3.4.1, is derived, which serves as an improved version of Thm. 4.4 in [KMW10]. The conditions of this theorem are shown to be weaker than those in [KMW10]. At the same time though, the conclusion does not require the approximating pair linear combinations to be quasimodes contained in the domain of the perturbed operator. Cor. 3.5.2 allows for a transparent comparison between the results of this Chapter and [KMW10]. Chapter 4 deals with the construction of non-singular rank-one perturbations for which the eigenvalues and eigenfunctions approximate those of the delta-perturbed operator. This is approached by means of direct analysis of the construction and formulae for the rank-one-perturbed eigenvalues and eigenfunctions, by comparison that of the delta-perturbed eigenvalues and eigenfunctions. Successful results are derived to this end, the central result being Thm. 4.4.19. This provides conditions on a sequence of non-singular rank-one perturbations, under which all eigenvalues and eigenbasis members within an interval converge to those of the delta-perturbed operator. Comparisons have also been drawn with previous literature such as [Zor80], [AK00] and [GN12]. These deal with rank-one perturbations approaching the delta potential within the setting of a whole Euclidean space Rⁿ, for example by strong resolvent convergence, and by limiting behaviour of generalised eigenfunctions associated with energies at every Eℓ(0,∞). Furthermore in Chapter 4, the suggestion from Chapter 2 that the delta potential has negative infinitessimal strength is further supported, due to the coefficients of the approximating rank-one perturbations being negative and tending to zero. This phenomenon is also in agreement with formulae from [Zor80], [AK00] and [GN12]. Chapter 5 first reviews the correspondence between certain classical dynamics and equidistribution in position space of almost all unperturbed quantum eigenfunctions, as demonstrated for example in [MR12]. Equidistribution in position space of almost all perturbed eigenfunctions, in the case of the 2D rectangular flat torus, is also reviewed. This result comes from [RU12], which is only stated in terms of the "new" perturbed eigenfunctions, which would only be a subset of the full perturbed eigenbasis. Nevertheless, in this Chapter it is explained how it follows that this position space equidistribution result also applies to a full-density subsequence of the full perturbed eigenbasis. Finally three methods of approach are discussed for attempting to derive this position space equidistribution result in the case of a more general delta-perturbed system whose classical dynamics satisfies the particular key property.

Orientadores: Carlile Campos Lavor, Leonardo Silva de Lima / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-26T08:31:47Z (GMT). No. of bitstreams: 1 Amaro_BrunoDias_D.pdf: 1369520 bytes, checksum: a36663d5fd23193d66bb22c83cb932aa (MD5) Previous issue date: 2014 / Resumo: A Teoria Espectral de Grafos é um ramo da Matemática Discreta que se preocupa com a relação entre as propriedades algébricas do espectro de certas matrizes associadas a grafos, como a matriz de adjacência, laplaciana ou laplaciana sem sinal e a topologia dos mesmos. Os autovalores e autovetores das matrizes associadas a um grafo são os invariantes que formam o autoespaço de grafos. Em Teoria Espectral de Grafos a conjectura proposta por Brouwer e Haemers, que associa a soma dos k maiores autovalores da matriz Laplaciana de um grafo G com seu número de arestas mais um fator combinatório (que depende do valor k adotado) é uma das questões interessantes e que está em aberto na literatura. Essa mostra diversos trabalhos que tentam provar tal conjectura. Em 2013, Ashraf et al. estenderam essa conjectura para a matriz laplaciana sem sinal e provaram que ela é válida para a soma dos 2 maiores autovalores e que também é válida para todo k, caso o grafo seja regular. Nosso trabalho aborda a versão dessa conjectura para a matriz laplaciana sem sinal. Conseguimos obter uma família de grafos que satisfaz a conjectura para a soma dos 3 maiores autovalores da matriz laplaciana sem sinal e a família de grafos split completo mais uma aresta satisfaz a conjectura para todos os autovalores. Ainda, baseado na desigualdade de Schur, conseguimos mostrar que a soma dos k menores autovalores das matrizes laplaciana e laplaciana sem sinal são limitadas superiormente pela soma dos k menores graus de G / Abstract: The Spectral Graph Theory is a branch of Discrete Mathematics that is concerned with relations between the algebraic properties of spectrum of some matrices associated to graphs, as the Adjacency, Laplacian and signless Laplacian matrices and their respective topologies. The eigenvalues and eigenvectors of matrices associated to graphs are the invariants which constitute the eigenspace of graphs. On Spectral Graph Theory the conjecture proposed by Brouwer and Haemers, associating the sum of k largest eigenvalues of Laplacian matrix of a graph G with its edges numbers plus a combinatorial factor (which depends on the choosed k) is an open interesting question in the Literature. There are several works that attempt to prove this conjecture. In 2013, Ashraf et al. stretch the conjecture out to signless Laplacian matrix and proved that it is true for the sum of the 2 largest eigenvalues of signless Laplacian matrix and it is also true for all k if G is a regular graph. Our work approaches on the version of the conjecture concerning to signless Laplacian matrix. We could obtain a family of graphs which satisfies the conjecture for the sum of the 3 largest eigenvalues of signless Laplacian matrix and we prove that the family of complete split graphs plus one edge satisfies the Conjecture for all eigenvalues. Moreover, based on Schur's inequality, we could show that the sum of the k smallest eigenvalues of Laplacian and signless Laplacian matrices are bounded by the sum of the k smallest degrees of G / Doutorado / Matematica Aplicada / Doutor em Matemática Aplicada

We characterize trees that have greatest maximum p-Laplacian eigenvalue among all trees with a given degree sequence. We show that such extremal trees can be obtained by breadth-first search where the vertex degrees are non-increasing. These trees are uniquely determined up to isomorphism. Moreover, their structure does not depend on p. / Series: Research Report Series / Department of Statistics and Mathematics

Dans la première partie de cette thèse, nous introduisons une nouvelle généralisation de l'opérateur de Laplace en géométrie de Finsler. Cette opérateur est défini en intégrant le long des fibres les dérivées directionnelles secondes d'une fonction par rapport à une mesure d'angle que nous construisons. Nous obtenons un opérateur différentiel d'ordre ..., elliptique, symétrique, et qui admet une bonne théorie spectrale. Nous calculons des exemples explicites de spectres pour des métriques de Katok-Ziller. En courbure négative, nous prouvons, grâce à un théorème d'Ancona que la frontière de Martin est Hölder-homéomorphe à la frontière visuelle. Ceci nous permet de déduire l'existence et l'ergodicité des mesures harmoniques pour cet opérateur. Dans la seconde partie, nous étudions les flots d'Anosov en dimension ... dont l'espace des feuilles est homéomorphe à .... Lorsque la variété est hyperbolique, Thurston démontra que le feuilletage (in)stable induit un flot ''orthogonal'' au premier. Nous utilisons ce second flot pour étudier les classes d'isotopie d'orbites périodiques du flot d'Anosov, ainsi que l'existence de cylindres plongés. / In the first part of this dissertation, we give a new definition of a Laplace operator for Finsler metric as an average, with regard to an angle measure, of the second directional derivatives. This operator is elliptic, symmetric with respect to the Holmes-Thompson volume, and coincides with the usual Laplace--Beltrami operator when the Finsler metric is Riemannian. We compute explicit spectral data for some Katok-Ziller metrics. When the Finsler metric is negatively curved, we show, thanks to a result of Ancona that the Martin boundary is Hölder-homeomorphic to the visual boundary. This allow us to deduce the existence of harmonic measures and some ergodic preoperties. In the second part of this dissertation, we study Anosov flows in 3-manifolds, with leaf-spaces homeomorphic to .... When the manifold is hyperbolic, Thurston showed that the (un)stable foliations induces an "orthogonal" flow. We use this second flow to study isotopy class of periodic orbits of the Anosov flow and existence of embedded cylinders.

Laplacian differential operator -- Vibrations of plucked strings and Hollow cylinders.

Neural Processes (NPs) are a class of regression models that learn a map from a set of input-output pairs to a distribution over functions. NPs are computationally tractable and provide a number of benefits over traditional nonlinear regression models. Despite these benefits, there are two main domains where NPs fail. This thesis is focused on presenting extensions of the Neural Process to these two areas. The first of these is the extension of Neural Processes graph and network data which we call Graph Neural Processes (GNP). A Graph Neural Process is defined as a Neural Process that operates on graph data. It takes spectral information from the graph Laplacian as inputs and then outputs a distribution over values. We demonstrate Graph Neural Processes in edge value imputation and discuss benefits and drawbacks of the method for other application areas. The second extension of Neural Processes comes in the fundamental training mechanism. NPs are traditionally trained using maximum likelihood, a probabilistic technique. We show that there are desirable classes of problems where NPs fail to learn. We also show that this drawback is solved by using approximations of the Wasserstein distance. We give experimental justification for our method and demonstrate its performance. These Wasserstein Neural Processes (WNPs) maintain the benefits of traditional NPs while being able to approximate new classes of function mappings.

The interdisciplinary field of machine learning studies algorithms in which functionality is dependent on data sets. This data is often treated as a matrix, and a variety of mathematical methods have been developed to glean information from this data structure such as matrix decomposition. The Laplacian matrix, for example, is commonly used to reconstruct networks, and the eigenpairs of this matrix are used in matrix decomposition. Moreover, concepts such as SVD matrix factorization are closely connected to manifold learning, a subfield of machine learning that assumes the observed data lie on a low-dimensional manifold embedded in a higher-dimensional space. Since many data sets have natural higher dimensions, tensor methods are being developed to deal with big data more efficiently. This thesis builds on these ideas by exploring how matrix methods can be extended to data presented as tensors rather than simply as ordinary vectors.

In this paper, we investigate the question of the spectrally equivalence of the so- called Pseudo-Laplacian to the usual discrete Laplacian in order to use hierarchical preconditioners for this more complicate matrix. The spectral equivalence is shown to be equivalent to a Brezzi-type inequality, which is fulfilled for the finite element spaces considered here.

This thesis presents an analysis of several smoothness energies (also called smoothing energies) in geometry processing, and introduces new methods as well as a mathematical proof of correctness and convergence for a well-established method. Geometry processing deals with the acquisition, modification, and output (be it on a screen, in virtual reality, or via fabrication and manufacturing) of complex geometric objects and data. It is closely related to computer graphics, but is also used by many other fields that employ applied mathematics in the context of geometry. The popular Laplacian energy is a smoothness energy that quantifies smoothness and that is closely related to the biharmonic equation (which gives it desirable properties). Minimizers of the Laplacian energy solve the biharmonic equation. This thesis provides a proof of correctness and convergence for a very popular discretization method for the biharmonic equation with zero Dirichlet and Neumann boundary conditions, the piecewise linear Lagrangian mixed finite element method. The same approach also discretizes the Laplacian energy. Such a proof has existed for flat surfaces for a long time, but there exists no such proof for the curved surfaces that are needed to represent the complicated geometries used in geometry processing. This proof will improve the usefulness of this discretization for the Laplacian energy. In this thesis, the novel Hessian energy for curved surfaces is introduced, which also quantifies the smoothness of a functions, and whose minimizers solve the biharmonic equation. This Hessian energy has natural boundary conditions that allow the construction of functions that are not significantly biased by the geometry and presence of boundaries in the domain (unlike the Laplacian energy with zero Neumann boundary conditions), while still conforming to constraints informed by the application. This is useful in any situation where the boundary of the domain is not an integral part of the problem itself, but just an artifact of data representation---be it, because of artifacts created by an imprecise scan of the surface, because information is missing outside of a certain region, or because the application simply demands a result that should not depend on the geometry of the boundary. Novel discretizations of this energy are also introduced and analyzed. This thesis also presents the new developability energy, which quantifies a different kind of smoothness than the Laplacian and Hessian energies: how easy is it to unfold a surface so that it lies flat on the plane without any distortion (surfaces for which this is possible are called developable surfaces). Developable surfaces are interesting, as they can be easily constructed from cheap material such as paper and plywood, or manufactured with methods such as 5-axis CNC milling. A novel definition of developability for discrete triangle meshes, as well as a variety of discrete developability energies are also introduced and applied to problems such as approximation of a surface by a piecewise developable surface, and the design and fabrication of piecewise developable surfaces. This will enable users to more easily take advantages of these cheap and quick fabrication methods. The novel methods, algorithms and the mathematical proof introduced in this thesis will be useful in many applications and fields, including numerical analysis of elliptic partial differential equations, geometry processing of triangle meshes, character animation, data denoising, data smoothing, scattered data interpolation, fabrication from simple materials, computer-controlled fabrication, and more.

The Discrete Nodal Domain Theorem states that an eigenfunction of the k-th largest eigenvalue of a generalized graph Laplacian has at most k (weak) nodal domains. We show that the number of strong nodal domains cannot exceed the size of a maximal induced bipartite subgraph and that this bound is sharp for generalized graph Laplacians. Similarly, the number of weak nodal domains is bounded by the size of a maximal bipartite minor.