Рассматривается обобщенное ядро Пуассона, представляющее собой линейную комбинацию ядра Пуассона и сопряженного ядра Пуассона. Найдены величины наилучшего интегрального приближения снизу и сверху этого ядра тригонометрическими полиномами порядка не выше заданного и соответствующие полиномы наилучшего одностороннего приближения. / We consider the generalized Poisson kernel,which is a linear combination of the Poisson kernel and the conjugate Poisson kernel. The values of the best integral approximations to this kernel from below and from above by trigonometric polynomials of degree not exceeding a given number has been found. The corresponding polynomials of the best one-sided approximation has been obtained.

We study the problem on the exact estimation of the value of the best mean-square approximation on the period to an arbitrary complex-valued periodic function by trigonometric polynomials of degree not exceeding a given number in terms of its nonclassical L2 -modulus of continuity generated by the finite-difference operator of order 2m-1, m>= 2, with alternating constant coefficients equal to 1 in absolute value. / Исследуется задача о точной оценке величины наилучшего среднеквадратического приближения на периоде произвольной комплекснозначной периодической функции тригонометрическими полиномами порядка не выше заданного через ее неклассический L2 -модуль непрерывности, порожденный конечно-разностным оператором порядка 2m-1, m>= 2, с постоянными знакочередующимися коэффициентами, равными по модулю единице.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Made available in DSpace on 2017-07-21T20:56:30Z (GMT). No. of bitstreams: 1 SUELLEN IOCHUCKI.pdf: 6944983 bytes, checksum: ba58e93c2b6c033ab9341effdee604c5 (MD5) Previous issue date: 2016-06-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work presents an approach to trigonometry content using Geogebra and Maxima programs. It provides a road map of how trigonometry can be worked in the classroom divided into stages . It proposes applications of trigonometry in different areas of knowledge and suggests the introduction of the approach of periodic functions by trigonometric polynomials . It is a qualitative and exploratory research . Its relevance is justified because it allows the teacher to look at the process of teaching and learning significantly to the student. During the development of research, the approach of trigonometry content was the history of mathematics and the use of software. The use of computing resources was an important ally to scientific knowledge, as well as the use of applications for the contextualization. / Este trabalho apresenta uma abordagem sobre o conteúdo de Trigonometria utilizando os programas Geogebra e Maxima. Traz um roteiro de como a Trigonometria pode ser trabalhada em sala de aula dividido em etapas. Propõe aplicações da Trigonometria em diferentes áreas do conhecimento e sugere a introdução da aproximação de funções periódicas por polinômios trigonométricos. Trata-se de uma pesquisa qualitativa e exploratória. A sua relevância justificase por possibilitar ao professor olhar o processo de ensino-aprendizagem de forma significativa ao aluno. Durante o desenvolvimento da pesquisa, a abordagem do conteúdo de trigonometria ocorreu pela história da matemática e com a utilização de softwares. A utilização dos recursos computacionais mostrou-se importante aliada ao saber científico, assim como a utilização de aplicações para a contextualização do tema.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

We consider multivariate trigonometric polynomials with frequencies supported on a fixed but arbitrary frequency index set I, which is a finite set of integer vectors of length d. Naturally, one is interested in spatial discretizations in the d-dimensional torus such that - the sampling values of the trigonometric polynomial at the nodes of this spatial discretization uniquely determines the trigonometric polynomial, - the corresponding discrete Fourier transform is fast realizable, and - the corresponding fast Fourier transform is stable. An algorithm that computes the discrete Fourier transform and that needs a computational complexity that is bounded from above by terms that are linear in the maximum of the number of input and output data up to some logarithmic factors is called fast Fourier transform. We call the fast Fourier transform stable if the Fourier matrix of the discrete Fourier transform has a condition number near one and the fast algorithm does not corrupt this theoretical stability. We suggest to use rank-1 lattices and a generalization as spatial discretizations in order to sample multivariate trigonometric polynomials and we develop construction methods in order to determine reconstructing sampling sets, i.e., sets of sampling nodes that allow for the unique, fast, and stable reconstruction of trigonometric polynomials. The methods for determining reconstructing rank-1 lattices are component{by{component constructions, similar to the seminal methods that are developed in the field of numerical integration. During this thesis we identify a component{by{component construction of reconstructing rank-1 lattices that allows for an estimate of the number of sampling nodes M |I|\le M\le \max\left(\frac{2}{3}|I|^2,\max\{3\|\mathbf{k}\|_\infty\colon\mathbf{k}\in I\}\right) that is sufficient in order to uniquely reconstruct each multivariate trigonometric polynomial with frequencies supported on the frequency index set I. We observe that the bounds on the number M only depends on the number of frequency indices contained in I and the expansion of I, but not on the spatial dimension d. Hence, rank-1 lattices are suitable spatial discretizations in arbitrarily high dimensional problems. Furthermore, we consider a generalization of the concept of rank-1 lattices, which we call generated sets. We use a quite different approach in order to determine suitable reconstructing generated sets. The corresponding construction method is based on a continuous optimization method. Besides the theoretical considerations, we focus on the practicability of the presented algorithms and illustrate the theoretical findings by means of several examples. In addition, we investigate the approximation properties of the considered sampling schemes. We apply the results to the most important structures of frequency indices in higher dimensions, so-called hyperbolic crosses and demonstrate the approximation properties by the means of several examples that include the solution of Poisson's equation as one representative of partial differential equations.

In this work, the fast evaluation and reconstruction of multivariate trigonometric polynomials with frequencies supported on arbitrary index sets of finite cardinality is considered, where rank-1 lattices are used as spatial discretizations. The approximation of multivariate smooth periodic functions by trigonometric polynomials is studied, based on a one-dimensional FFT applied to function samples. The smoothness of the functions is characterized via the decay of their Fourier coefficients, and various estimates for sampling errors are shown, complemented by numerical tests for up to 25 dimensions. In addition, the special case of perturbed rank-1 lattice nodes is considered, and a fast Taylor expansion based approximation method is developed. One main contribution is the transfer of the methods to the non-periodic case. Multivariate algebraic polynomials in Chebyshev form are used as ansatz functions and rank-1 Chebyshev lattices as spatial discretizations. This strategy allows for using fast algorithms based on a one-dimensional DCT. The smoothness of a function can be characterized via the decay of its Chebyshev coefficients. From this point of view, estimates for sampling errors are shown as well as numerical tests for up to 25 dimensions. A further main contribution is the development of a high-dimensional sparse FFT method based on rank-1 lattice sampling, which allows for determining unknown frequency locations belonging to the approximately largest Fourier or Chebyshev coefficients of a function. / In dieser Arbeit wird die schnelle Auswertung und Rekonstruktion multivariater trigonometrischer Polynome mit Frequenzen aus beliebigen Indexmengen endlicher Kardinalität betrachtet, wobei Rang-1-Gitter (rank-1 lattices) als Diskretisierung im Ortsbereich verwendet werden. Die Approximation multivariater glatter periodischer Funktionen durch trigonometrische Polynome wird untersucht, wobei Approximanten mittels einer eindimensionalen FFT (schnellen Fourier-Transformation) angewandt auf Funktionswerte ermittelt werden. Die Glattheit von Funktionen wird durch den Abfall ihrer Fourier-Koeffizienten charakterisiert und mehrere Abschätzungen für den Abtastfehler werden gezeigt, ergänzt durch numerische Tests für bis zu 25 Raumdimensionen. Zusätzlich wird der Spezialfall gestörter Rang-1-Gitter-Knoten betrachtet, und es wird eine schnelle Approximationsmethode basierend auf Taylorentwicklung vorgestellt. Ein wichtiger Beitrag dieser Arbeit ist die Übertragung der Methoden vom periodischen auf den nicht-periodischen Fall. Multivariate algebraische Polynome in Chebyshev-Form werden als Ansatzfunktionen verwendet und sogenannte Rang-1-Chebyshev-Gitter als Diskretisierungen im Ortsbereich. Diese Strategie ermöglicht die Verwendung schneller Algorithmen basierend auf einer eindimensionalen DCT (diskreten Kosinustransformation). Die Glattheit von Funktionen kann durch den Abfall ihrer Chebyshev-Koeffizienten charakterisiert werden. Unter diesem Gesichtspunkt werden Abschätzungen für Abtastfehler gezeigt sowie numerische Tests für bis zu 25 Raumdimensionen. Ein weiterer wichtiger Beitrag ist die Entwicklung einer Methode zur Berechnung einer hochdimensionalen dünnbesetzten FFT basierend auf Abtastwerten an Rang-1-Gittern, wobei diese Methode die Bestimmung unbekannter Frequenzen ermöglicht, welche zu den näherungsweise größten Fourier- oder Chebyshev-Koeffizienten einer Funktion gehören.

We consider multivariate trigonometric polynomials with frequencies supported on a fixed but arbitrary frequency index set I, which is a finite set of integer vectors of length d. Naturally, one is interested in spatial discretizations in the d-dimensional torus such that - the sampling values of the trigonometric polynomial at the nodes of this spatial discretization uniquely determines the trigonometric polynomial, - the corresponding discrete Fourier transform is fast realizable, and - the corresponding fast Fourier transform is stable. An algorithm that computes the discrete Fourier transform and that needs a computational complexity that is bounded from above by terms that are linear in the maximum of the number of input and output data up to some logarithmic factors is called fast Fourier transform. We call the fast Fourier transform stable if the Fourier matrix of the discrete Fourier transform has a condition number near one and the fast algorithm does not corrupt this theoretical stability. We suggest to use rank-1 lattices and a generalization as spatial discretizations in order to sample multivariate trigonometric polynomials and we develop construction methods in order to determine reconstructing sampling sets, i.e., sets of sampling nodes that allow for the unique, fast, and stable reconstruction of trigonometric polynomials. The methods for determining reconstructing rank-1 lattices are component{by{component constructions, similar to the seminal methods that are developed in the field of numerical integration. During this thesis we identify a component{by{component construction of reconstructing rank-1 lattices that allows for an estimate of the number of sampling nodes M |I|\le M\le \max\left(\frac{2}{3}|I|^2,\max\{3\|\mathbf{k}\|_\infty\colon\mathbf{k}\in I\}\right) that is sufficient in order to uniquely reconstruct each multivariate trigonometric polynomial with frequencies supported on the frequency index set I. We observe that the bounds on the number M only depends on the number of frequency indices contained in I and the expansion of I, but not on the spatial dimension d. Hence, rank-1 lattices are suitable spatial discretizations in arbitrarily high dimensional problems. Furthermore, we consider a generalization of the concept of rank-1 lattices, which we call generated sets. We use a quite different approach in order to determine suitable reconstructing generated sets. The corresponding construction method is based on a continuous optimization method. Besides the theoretical considerations, we focus on the practicability of the presented algorithms and illustrate the theoretical findings by means of several examples. In addition, we investigate the approximation properties of the considered sampling schemes. We apply the results to the most important structures of frequency indices in higher dimensions, so-called hyperbolic crosses and demonstrate the approximation properties by the means of several examples that include the solution of Poisson's equation as one representative of partial differential equations.

In this work, the fast evaluation and reconstruction of multivariate trigonometric polynomials with frequencies supported on arbitrary index sets of finite cardinality is considered, where rank-1 lattices are used as spatial discretizations. The approximation of multivariate smooth periodic functions by trigonometric polynomials is studied, based on a one-dimensional FFT applied to function samples. The smoothness of the functions is characterized via the decay of their Fourier coefficients, and various estimates for sampling errors are shown, complemented by numerical tests for up to 25 dimensions. In addition, the special case of perturbed rank-1 lattice nodes is considered, and a fast Taylor expansion based approximation method is developed. One main contribution is the transfer of the methods to the non-periodic case. Multivariate algebraic polynomials in Chebyshev form are used as ansatz functions and rank-1 Chebyshev lattices as spatial discretizations. This strategy allows for using fast algorithms based on a one-dimensional DCT. The smoothness of a function can be characterized via the decay of its Chebyshev coefficients. From this point of view, estimates for sampling errors are shown as well as numerical tests for up to 25 dimensions. A further main contribution is the development of a high-dimensional sparse FFT method based on rank-1 lattice sampling, which allows for determining unknown frequency locations belonging to the approximately largest Fourier or Chebyshev coefficients of a function. / In dieser Arbeit wird die schnelle Auswertung und Rekonstruktion multivariater trigonometrischer Polynome mit Frequenzen aus beliebigen Indexmengen endlicher Kardinalität betrachtet, wobei Rang-1-Gitter (rank-1 lattices) als Diskretisierung im Ortsbereich verwendet werden. Die Approximation multivariater glatter periodischer Funktionen durch trigonometrische Polynome wird untersucht, wobei Approximanten mittels einer eindimensionalen FFT (schnellen Fourier-Transformation) angewandt auf Funktionswerte ermittelt werden. Die Glattheit von Funktionen wird durch den Abfall ihrer Fourier-Koeffizienten charakterisiert und mehrere Abschätzungen für den Abtastfehler werden gezeigt, ergänzt durch numerische Tests für bis zu 25 Raumdimensionen. Zusätzlich wird der Spezialfall gestörter Rang-1-Gitter-Knoten betrachtet, und es wird eine schnelle Approximationsmethode basierend auf Taylorentwicklung vorgestellt. Ein wichtiger Beitrag dieser Arbeit ist die Übertragung der Methoden vom periodischen auf den nicht-periodischen Fall. Multivariate algebraische Polynome in Chebyshev-Form werden als Ansatzfunktionen verwendet und sogenannte Rang-1-Chebyshev-Gitter als Diskretisierungen im Ortsbereich. Diese Strategie ermöglicht die Verwendung schneller Algorithmen basierend auf einer eindimensionalen DCT (diskreten Kosinustransformation). Die Glattheit von Funktionen kann durch den Abfall ihrer Chebyshev-Koeffizienten charakterisiert werden. Unter diesem Gesichtspunkt werden Abschätzungen für Abtastfehler gezeigt sowie numerische Tests für bis zu 25 Raumdimensionen. Ein weiterer wichtiger Beitrag ist die Entwicklung einer Methode zur Berechnung einer hochdimensionalen dünnbesetzten FFT basierend auf Abtastwerten an Rang-1-Gittern, wobei diese Methode die Bestimmung unbekannter Frequenzen ermöglicht, welche zu den näherungsweise größten Fourier- oder Chebyshev-Koeffizienten einer Funktion gehören.