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E-raamat: Multivariate Approximation

(University of South Carolina)
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This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers substantial new developments in the linear approximation theory of classes with mixed smoothness, and shows how it is directly related to deep problems in other areas of mathematics. For example, numerical integration of these classes is closely related to discrepancy theory and to nonlinear approximation with respect to special redundant dictionaries, and estimates of the entropy numbers of classes with mixed smoothness are closely related to (in some cases equivalent to) the Small Ball Problem from probability theory. The useful background material included in the book makes it accessible to graduate students. Researchers will find that the many open problems in the theory outlined in the book provide helpful directions and guidance for their own research in this exciting and active area.

Starting from classical linear approximation, this is a self-contained presentation of modern multivariate approximation theory that explores its connections with other areas of mathematics. The prerequisites are no more than standard undergraduate mathematics, so the book will be accessible to graduate students and non-specialists.

Arvustused

'This excellent book covers a variety of topics in univariate and multivariate approximation as well as their connection to computational mathematics The exposition is designed in such a way that the reader is familiarized with the univariate results rst and then the transition to the multivariate case is performed, highlighting the challenges and the new methods. The book is self-contained and is accessible to readers with graduate and advanced undergraduate background.' Andriy V. Prymak, MathSciNet

Muu info

Self-contained presentation of multivariate approximation from classical linear approximation to contemporary nonlinear approximation.
Preface ix
1 Approximation of Univariate Functions
1(35)
1.1 Introduction
1(5)
1.2 Trigonometric Polynomials
6(11)
1.3 The Bernstein-Nikol'skii Inequalities. The Marcienkiewicz Theorem
17(8)
1.4 Approximation of Functions in the Classes Wrq,α and Hrq
25(9)
1.5 Historical Remarks
34(2)
2 Optimality and Other Properties of the Trigonometric System
36(45)
2.1 The Widths of the Classes Wrq,α and Hrq
36(18)
2.2 Further Properties of the Trigonometric System
54(8)
2.3 Approximation of Functions with Infinite Smoothness
62(11)
2.4 Sampling and Numerical Integration
73(6)
2.5 Historical Remarks
79(2)
3 Approximation of Functions from Anisotropic Sobolev and Nikol'skii Classes
81(48)
3.1 Introduction
81(1)
3.2 Trigonometric Polynomials
82(7)
3.3 The Bernstein--Nikol'skii Inequalities and Their Applications. A Generalization of the Marcinkiewicz Theorem
89(15)
3.4 Approximation of Functions in the Classes Wrq,α and Hrq
104(9)
3.5 Estimates of the Widths of the Sobolev and Nikol'skii Classes
113(8)
3.6 Sampling and Numerical Integration
121(5)
3.7 Historical Remarks
126(3)
4 Hyperbolic Cross Approximation
129(62)
4.1 Introduction
129(9)
4.2 Some Special Polynomials with Harmonics in Hyperbolic Crosses
138(13)
4.3 The Bernstein--Nikol'skii Inequalities
151(11)
4.4 Approximation of Functions in the Classes Wrq,α and Wrq
162(24)
4.5 Some Further Remarks
186(3)
4.6 Historical Comments
189(1)
4.7 Open Problems
190(1)
5 The Widths of Classes of Functions with Mixed Smoothness
191(53)
5.1 Introduction
191(2)
5.2 The Orthowidths of the Classes Wrq,α and Hrq
193(23)
5.3 The Kolmogorov Widths of the Classes Wrq,α and Hrq
216(15)
5.4 Universality of Approximation by Trigonometric Polynomials from the Hyperbolic Crosses
231(10)
5.5 Historical Remarks
241(1)
5.6 Open Problems
242(2)
6 Numerical Integration and Approximate Recovery
244(77)
6.1 Introduction
244(2)
6.2 Cubature Formulas and Discrepancy
246(7)
6.3 Optimal Cubature Formulas and Nonlinear Approximation
253(9)
6.4 Lower Estimates
262(10)
6.5 The Fibonacci Cubature Formulas
272(12)
6.6 The Korobov Cubature Formulas
284(5)
6.7 The Frolov Cubature Formulas
289(13)
6.8 Universal Cubature Formulas
302(3)
6.9 Recovery of Functions
305(10)
6.10 Historical Notes, Comments, and Some Open Problems
315(5)
6.11 Open Problems
320(1)
7 Entropy
321(66)
7.1 Introduction. Definitions and Some Simple Properties
321(2)
7.2 Finite-Dimensional Spaces. Volume Estimates
323(2)
7.3 Some Simple General Inequalities
325(3)
7.4 An Inequality Between Entropy Numbers and Best m-Term Approximations
328(5)
7.5 Volume Estimates for Balls of Trigonometric Polynomials
333(12)
7.6 Entropy Numbers of the Balls of Trigonometric Polynomials
345(18)
7.7 Entropy Numbers for the W-Type Function Classes
363(10)
7.8 Entropy Numbers for the H-Type Function Classes
373(7)
7.9 Discussion and Open Problems
380(3)
7.10 Some Historical Comments
383(4)
8 Greedy Approximation
387(62)
8.1 Introduction
387(7)
8.2 The Trigonometric System
394(4)
8.3 Wavelet Bases
398(6)
8.4 Some Inequalities for the Tensor Product of Greedy Bases
404(8)
8.5 Weight-Greedy Bases
412(3)
8.6 The Weak Chebyshev Greedy Algorithm
415(7)
8.7 Sparse Approximation With Respect to General Dictionaries
422(25)
8.8 Open Problems
447(2)
9 Sparse Approximation
449(51)
9.1 Introduction
449(5)
9.2 Constructive Sparse Trigonometric Approximation
454(18)
9.3 Constructive Sparse Trigonometric Approximation for Small Smoothness
472(23)
9.4 Open Problems
495(1)
9.5 Concluding Remarks
496(4)
Appendix Classical Inequalities 500(20)
References 520(12)
Index 532
V. Temlyakov is Carolina Distinguished Professor in the Department of Mathematics at the University of South Carolina. He has written several books on approximation theory, and has received numerous honours and awards. His research interests include greedy approximation, compressed sensing, learning theory and numerical integration.