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Approximation Theory and Algorithms for Data Analysis, 1st ed. 2018 Texts in Applied Mathematics Series, Vol. 68

Langue : Anglais

Auteur :

Couverture de l’ouvrage Approximation Theory and Algorithms for Data Analysis

This textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics of approximation with recent advances in mathematical signal processing, and adopting a constructive approach, in which the development of numerical algorithms for data analysis plays an important role.

The following topics are covered:

* least-squares approximation and regularization methods

* interpolation by algebraic and trigonometric polynomials

* basic results on best approximations

* Euclidean approximation

* Chebyshev approximation

* asymptotic concepts: error estimates and convergence rates

* signal approximation by Fourier and wavelet methods

* kernel-based multivariate approximation

* approximation methods in computerized tomography

Providing numerous supporting examples, graphical illustrations, and carefully selected exercises, this textbook is suitable for introductory courses, seminars, and distance learning programs on approximation for undergraduate students.


1 Introduction.- 2 Basic Methods and Numerical Analysis.- 3 Best Approximations.- 4 Euclidean Approximations.- 5 Chebyshev Approximations.- 6 Asymptotic Results.- 7 Basic Concepts of Signal Approximation.- 8 Kernel-Based Approximation.- 9 Computational Topology.- References.- Subject Index.- Name Index.
Armin Iske is professor of numerical approximation at the Department of Mathematics of the University of Hamburg.

Clear and comprehensible introduction to approximation theory and its applications

Offers a constructive approach to methods and algorithms

Contains a large number of examples and exercises

Date de parution :

Ouvrage de 358 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

63,29 €

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