No description available.

Vectors and matrices over the Boolean (0,1) semiring have been studied extensively along with their applications to graph theory. The Boolean (0,1) semiring has been generalized to a class of semirings called chain semirings. This class includes the fuzzy interval. Vectors and matrices over chain semirings are examined. Rank-1 sets of vectors are defined and characterized. These rank-1 sets of vectors are then used to construct spaces of matrices (rank-1 spaces) with the property that all nonzero matrices in the space have semiring rank equal to 1. Finally, three classes of maximal (relative to containment) rank-1 spaces are identified.

viii, 107 p. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / This dissertation consists of two related parts. In the first portion we use the tracial Rokhlin property for actions of a finite group G on stably finite simple unital C *-algebras containing enough projections. The main results of this part of the dissertation are as follows. Let A be a stably finite simple unital C *-algebra and suppose a is an action of a finite group G with the tracial Rokhlin property. Suppose A has real rank zero, stable rank one, and suppose the order on projections over A is determined by traces. Then the crossed product algebra C * ( G, A, à à ±) also has these three properties. In the second portion of the dissertation we introduce an analogue of the tracial Rokhlin property for C *-algebras which may not have any nontrivial projections called the projection free tracial Rokhlin property . Using this we show that under certain conditions if A is an infinite dimensional simple unital C *-algebra with stable rank one and à à ± is an action of a finite group G with the projection free tracial Rokhlin property, then C * ( G, A, à à ±) also has stable rank one. / Adviser: Phillips, N. Christopher

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.

No description available.

This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which increases the convergence rate by an order of magnitude compared with previous methods. In addition to closed loop control, open loop procedures using the SVD and SNMF are compared with previous scheduling procedures, demonstrating significant performance improvements. This thesis presents theoretical results for the controllability of systems using the row-column structure and for the stability and performance of the SVD and SNMF Systems. Practical challenges to the implementation of the SVD and SNMF Systems are also examined. Numerous simulation examples are provided, in particular, a dynamic simulation of a pin array device, called Digital Clay, and two physical demonstrations are used to assess the feasibility of the SVD and SNMF Systems for specific applications.

On s’intéresse dans cette thèse à la PGD (Proper Generalized Decomposition), l’une des méthodes de réduction de modèles qui consiste à chercher, a priori, la solution d’une équation aux dérivées partielles sous forme de variables séparées. Ce travail est formé de cinq chapitres dans lesquels on vise à étendre la PGD aux espaces fractionnaires et aux espaces des fonctions à variation bornée, et à donner des interprétations théoriques de cette méthode pour une classe de problèmes elliptiques et paraboliques. Dans le premier chapitre, on fait un bref aperçu sur la littérature puis on présente les notions et outils mathématiques utilisés dans le corps de la thèse. Dans le second chapitre, la convergence des suites des directions alternées (AM) pour une classe de problèmes variationnels elliptiques est étudiée. Sous une condition de non-orthogonalité uniforme entre les itérés et le terme source, on montre que ces suites sont en général bornées et compactes. Alors, si en particulier la suite (AM) converge faiblement alors elle converge fortement et la limite serait la solution du problème de minimisation alternée. Dans le troisième chapitre, on introduit la notion des dérivées fractionnaires au sens de Riemann-Liouville puis on considère un problème variationnel qui est une généralisation d’ordre fractionnaire de l’équation de Poisson. En se basant sur la nature quadratique et la décomposabilité de l’énergie associée, on démontre que la suite PGD progressive converge fortement vers la solution faible de ce problème. Dans le quatrième chapitre, on profite de la structure tensorielle des espaces BV par rapport à la topologie faible étoile pour définir les suites PGD dans ce type d’espaces. La convergence de telle suite reste une question ouverte. Le dernier chapitre est consacré à l’équation de la chaleur d-dimensionnelle, où on discrétise en temps puis à chaque pas de temps on cherche la solution de l’équation elliptique en utilisant la PGD. On montre alors que la fonction affine par morceaux en temps obtenue à partir des solutions construites en utilisant la PGD converge vers la solution faible de l’équation. / In this thesis, we are interested in the PGD (Proper Generalized Decomposition), one of the reduced order models which consists in searching, a priori, the solution of a partial differential equation in a separated form. This work is composed of five chapters in which we aim to extend the PGD to the fractional spaces and the spaces of functions of bounded variation and to give theoretical interpretations of this method for a class of elliptic and parabolic problems. In the first chapter, we give a brief review of the litterature and then we introduce the mathematical notions and tools used in this work. In the second chapter, the convergence of rank-one alternating minimisation AM algorithms for a class of variational linear elliptic equations is studied. We show that rank-one AM sequences are in general bounded in the ambient Hilbert space and are compact if a uniform non-orthogonality condition between iterates and the reaction term is fulfilled. In particular, if a rank-one (AM) sequence is weakly convergent then it converges strongly and the common limit is a solution of the alternating minimization problem. In the third chapter, we introduce the notion of fractional derivatives in the sense of Riemann-Liouville and then we consider a variational problem which is a generalization of fractional order of the Poisson equation. Basing on the quadratic nature and the decomposability of the associated energy, we prove that the progressive PGD sequence converges strongly towards the weak solution of this problem. In the fourth chapter, we benefit from tensorial structure of the spaces BV with respect to the weak-star topology to define the PGD sequences in this type of spaces. The convergence of this sequence remains an open question. The last chapter is devoted to the d-dimensional heat equation, we discretize in time and then at each time step one seeks the solution of the elliptic equation using the PGD. Then, we show that the piecewise linear function in time obtained from the solutions constructed using the PGD converges to the weak solution of the equation.

No description available.

Le sujet de cette thèse porte sur la résolution d'équations d'opérateurs dans l'algèbre B(H) des opérateurs linéaires bornés sur un espace de Hilbert H. Nous étudié celles qui sont associées aux dérivations généralisées. Mon sujet de thèse explore aussi des équations beaucoup plus générales comme celles du type AXB - XD = E ou AXB - CXD = E où A, B, C, D et E appartiennent à B(H). Plus précisément il s'agit de donner une description des solutions de ces équations pour E appartenant à une famille précise(autoadjoint, normal, rang un, rang fini, compact, couple de Fuglède Putnam) et pour des opérateurs A, B, C et D appartenant à des bonnes classes d'opérateurs ( celles qui interviennent dans les applications, notamment en physique) comme les opérateurs autoadjoints, les opérateurs normaux, sous normaux,... En dehors du cas où les spectres de A et B sont disjoints, il n'existe pas de méthode générale pour construire de manière effective l'ensemble des solutions de l'équation de Sylvester AX - XB = C à partir des opérateurs A, B et C. Un des objectifs de mon travail de thèse est de fournir une méthode constructive dans le cas où A, B et C appartiennent à des bonnes classes d'opérateurs. Une étude spectrale des solutions est également faite. A coté de cette étude qualitative, il y a aussi une étude quantitative.Il s'agit d'obtenir aussi des estimations précises de la norme d'opérateur(ou norme de Schatten) des solutions en fonction des normes des opérateurs correspondants aux données. Ceci nous a d'ailleurs conduit à des résultats concernant quelques inégalités intéressantes pour les dérivations généralisées, et enfin quelques résultats concernant les opérateurs dans un espace de Banach sont également donnés / The subject of this thesis focuses on the resolution of operator equationsin B(H) algebra of bounded linear operators on a Hilbert space. We studythose associated with generalized derivations. In this thesis, we also exploremore general equations such as the type AXB - XD = E or AXB -CXD = E where A, B, C, D and E belong to B(H). Specifically it is adescription of the solutions of these equations for E belongs in a precisefamily (Self-adjoint, normal, rank one, finite rank, compact, pair of FugledePutnam) and the operators A, B, C and D belonging to the good classesof operators (Those involved in applications , especially in physics) as theself-adjoint operators, normal operators, subnormal operators... Apart fromthe case where the spectra of A and B are disjoint, there is not any generalmethod for constructing effectively all solutions of the Sylvester equationAX - XB = C from the given operators A, B and C. One objective of thisthesis is to provide a constructive approach in when A, B and C belong toconventional families of operators. A spectral study of the solutions is alsostudied. Besides this qualitative study, there is also a quantitative study.It is also to obtain accurate estimates of the operator norm (or norm ofSchatten) of the solutions in terms of operator norms corresponding to data.This also led us to obtain results concerning some interesting inequalitiesfor generalized derivations, and finally some examples and properties ofoperators on a Banach space are also given

Tesis por compendio / [ES] La idea de seguir una secuencia de pasos para lograr un resultado deseado es inherente a la naturaleza humana: desde que empezamos a andar, siguiendo una receta de cocina o aprendiendo un nuevo juego de cartas. Desde la antigüedad se ha seguido este esquema para organizar leyes, corregir escritos, e incluso asignar diagnósticos. En matemáticas a esta forma de pensar se la denomina 'algoritmo'. Formalmente, un algoritmo es un conjunto de instrucciones definidas y no-ambiguas, ordenadas y finitas, que permite solucionar un problema. Desde pequeños nos enfrentamos a ellos cuando aprendemos a multiplicar o dividir, y a medida que crecemos, estas estructuras nos permiten resolver diferentes problemas cada vez más complejos: sistemas lineales, ecuaciones diferenciales, problemas de optimización, etcétera. Hay multitud de algoritmos que nos permiten hacer frente a este tipo de problemas, como métodos iterativos, donde encontramos el famoso Método de Newton para buscar raíces; algoritmos de búsqueda para localizar un elemento con ciertas propiedades en un conjunto mayor; o descomposiciones matriciales, como la descomposición LU para resolver sistemas lineales. Sin embargo, estos enfoques clásicos presentan limitaciones cuando se enfrentan a problemas de grandes dimensiones, problema que se conoce como `la maldición de la dimensionalidad'. El avance de la tecnología, el uso de redes sociales y, en general, los nuevos problemas que han aparecido con el desarrollo de la Inteligencia Artificial, ha puesto de manifiesto la necesidad de manejar grandes cantidades de datos, lo que requiere el diseño de nuevos mecanismos que permitan su manipulación. En la comunidad científica, este hecho ha despertado el interés por las estructuras tensoriales, ya que éstas permiten trabajar eficazmente con problemas de grandes dimensiones. Sin embargo, la mayoría de métodos clásicos no están pensados para ser empleados junto a estas operaciones, por lo que se requieren herramientas específicas que permitan su tratamiento, lo que motiva un proyecto como este. El presente trabajo se divide de la siguiente manera: tras revisar algunas definiciones necesarias para su comprensión, en el Capítulo 3, se desarrolla la teoría de una nueva descomposición tensorial para matrices cuadradas. A continuación, en el Capítulo 4, se muestra una aplicación de dicha descomposición a grafos regulares y redes de mundo pequeño. En el Capítulo 5, se plantea una implementación eficiente del algoritmo que proporciona la nueva descomposición matricial, y se estudian como aplicación algunas EDP de orden dos. Por último, en los Capítulos 6 y 7 se exponen unas breves conclusiones y se enumeran algunas de las referencias consultadas, respectivamente. / [CA] La idea de seguir una seqüència de passos per a aconseguir un resultat desitjat és inherent a la naturalesa humana: des que comencem a caminar, seguint una recepta de cuina o aprenent un nou joc de cartes. Des de l'antiguitat s'ha seguit aquest esquema per a organitzar lleis, corregir escrits, i fins i tot assignar diagnòstics. En matemàtiques a aquesta manera de pensar se la denomina algorisme. Formalment, un algorisme és un conjunt d'instruccions definides i no-ambigües, ordenades i finites, que permet solucionar un problema. Des de xicotets ens enfrontem a ells quan aprenem a multiplicar o dividir, i a mesura que creixem, aquestes estructures ens permeten resoldre diferents problemes cada vegada més complexos: sistemes lineals, equacions diferencials, problemes d'optimització, etcètera. Hi ha multitud d'algorismes que ens permeten fer front a aquesta mena de problemes, com a mètodes iteratius, on trobem el famós Mètode de Newton per a buscar arrels; algorismes de cerca per a localitzar un element amb unes certes propietats en un conjunt major; o descomposicions matricials, com la descomposició DL. per a resoldre sistemes lineals. No obstant això, aquests enfocaments clàssics presenten limitacions quan s'enfronten a problemes de grans dimensions, problema que es coneix com `la maledicció de la dimensionalitat'. L'avanç de la tecnologia, l'ús de xarxes socials i, en general, els nous problemes que han aparegut amb el desenvolupament de la Intel·ligència Artificial, ha posat de manifest la necessitat de manejar grans quantitats de dades, la qual cosa requereix el disseny de nous mecanismes que permeten la seua manipulació. En la comunitat científica, aquest fet ha despertat l'interés per les estructures tensorials, ja que aquestes permeten treballar eficaçment amb problemes de grans dimensions. No obstant això, la majoria de mètodes clàssics no estan pensats per a ser emprats al costat d'aquestes operacions, per la qual cosa es requereixen eines específiques que permeten el seu tractament, la qual cosa motiva un projecte com aquest. El present treball es divideix de la següent manera: després de revisar algunes definicions necessàries per a la seua comprensió, en el Capítol 3, es desenvolupa la teoria d'una nova descomposició tensorial per a matrius quadrades. A continuació, en el Capítol 4, es mostra una aplicació d'aquesta descomposició a grafs regulars i xarxes de món xicotet. En el Capítol 5, es planteja una implementació eficient de l'algorisme que proporciona la nova descomposició matricial, i s'estudien com a aplicació algunes EDP d'ordre dos. Finalment, en els Capítols 6 i 7 s'exposen unes breus conclusions i s'enumeren algunes de les referències consultades, respectivament. / [EN] The idea of following a sequence of steps to achieve a desired result is inherent in human nature: from the moment we start walking, following a cooking recipe or learning a new card game. Since ancient times, this scheme has been followed to organize laws, correct writings, and even assign diagnoses. In mathematics, this way of thinking is called an algorithm. Formally, an algorithm is a set of defined and unambiguous instructions, ordered and finite, that allows for solving a problem. From childhood, we face them when we learn to multiply or divide, and as we grow, these structures will enable us to solve different increasingly complex problems: linear systems, differential equations, optimization problems, etc. There is a multitude of algorithms that allow us to deal with this type of problem, such as iterative methods, where we find the famous Newton Method to find roots; search algorithms to locate an element with specific properties in a more extensive set; or matrix decompositions, such as the LU decomposition to solve some linear systems. However, these classical approaches have limitations when faced with large-dimensional problems, a problem known as the `curse of dimensionality'. The advancement of technology, the use of social networks and, in general, the new problems that have appeared with the development of Artificial Intelligence, have revealed the need to handle large amounts of data, which requires the design of new mechanisms that allow its manipulation. This fact has aroused interest in the scientific community in tensor structures since they allow us to work efficiently with large-dimensional problems. However, most of the classic methods are not designed to be used together with these operations, so specific tools are required to allow their treatment, which motivates work like this. This work is divided as follows: after reviewing some definitions necessary for its understanding, in Chapter 3, the theory of a new tensor decomposition for square matrices is developed. Next, Chapter 4 shows an application of said decomposition to regular graphs and small-world networks. In Chapter 5, an efficient implementation of the algorithm provided by the new matrix decomposition is proposed, and some order two PDEs are studied as an application. Finally, Chapters 6 and 7 present some brief conclusions and list some of the references consulted. / María Mora Jiménez acknowledges funding from grant (ACIF/2020/269) funded by the Generalitat Valenciana and the European Social Found / Mora Jiménez, M. (2023). Numerical approximations with tensor-based techniques for high-dimensional problems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202604 / Compendio