Description
Efnisyfirlit
- Preface
- Table of Contents
- 1 Introduction
- 1.1 Preliminaries, Definitions and Notations
- 1.2 Basic Problems and Outlook
- 1.3 Approximation Methods for Data Analysis
- 1.4 Hints on Classical and More Recent Literature
- 2 Basic Methods and Numerical Algorithms
- 2.1 Linear Least Squares Approximation
- 2.2 Regularization Methods
- 2.3 Interpolation by Algebraic Polynomials
- 2.4 Divided Differences and the Newton Representation
- 2.5 Error Estimates and Optimal Interpolation Points
- 2.6 Interpolation by Trigonometric Polynomials
- 2.7 The Discrete Fourier Transform
- 3 Best Approximations
- 3.1 Existence
- 3.2 Uniqueness
- 3.3 Dual Characterization
- 3.4 Direct Characterization
- 3.5 Exercises
- 4 Euclidean Approximation
- 4.1 Construction of Best Approximations
- 4.2 Orthogonal Bases and Orthogonal Projections
- 4.3 Fourier Partial Sums
- 4.4 Orthogonal Polynomials
- 4.5 Exercises
- 5 Chebyshev Approximation
- 5.1 Approaches to Construct Best Approximations
- 5.2 Strongly Unique Best Approximations
- 5.3 Haar Spaces
- 5.4 The Remez Algorithm
- 5.5 Exercises
- 6 Asymptotic Results
- 6.1 The Weierstrass Theorem
- 6.2 Complete Orthogonal Systems and Riesz Bases
- 6.3 Convergence of Fourier Partial Sums
- 6.4 The Jackson Theorems
- 6.5 Exercises
- 7 Basic Concepts of Signal Approximation
- 7.1 The Continuous Fourier Transform
- 7.2 The Fourier Transform on L2(R)
- 7.3 The Shannon Sampling Theorem
- 7.4 The Multivariate Fourier Transform
- 7.5 The Haar Wavelet
- 7.6 Exercises
- 8 Kernel-based Approximation
- 8.1 Multivariate Lagrange Interpolation
- 8.2 Native Reproducing Kernel Hilbert Spaces
- 8.3 Optimality of the Interpolation Method
- 8.4 Orthonormal Systems, Convergence, and Updates
- 8.5 Stability of the Reconstruction Scheme
- 8.6 Kernel-based Learning Methods
- 8.7 Exercises
- 9 Computerized Tomography
- 9.1 The Radon Transform
- 9.2 The Filtered Back Projection
- 9.3 Construction of Low-Pass Filters
- 9.4 Error Estimates and Convergence Rates
- 9.5 Implementation of the Reconstruction Method
- 9.6 Exercises
- References
- Subject Index
- Name Index
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