The theory of integral equations has been an active research field for many years and is based on analysis, function theory, and functional analysis. On the other hand, integral equations are of practical interest because of the 'boundary integral equation method', which transforms partial differential equations on a domain into integral equations over its boundary. This book grew out of a series of lectures given by the author at the Ruhr-Universitat Bochum and the Christian-Albrecht-Universitat zu Kiel to students of mathematics. The contents of the first six chapters correspond to an intensive lecture course of four hours per week for a semester. Readers of the book require background from analysis and the foundations of numeri cal mathematics. Knowledge of functional analysis is helpful, but to begin with some basic facts about Banach and Hilbert spaces are sufficient. The theoretical part of this book is reduced to a minimum; in Chapters 2, 4, and 5 more importance is attached to the numerical treatment of the integral equations than to their theory. Important parts of functional analysis (e. g., the Riesz-Schauder theory) are presented without proof. We expect the reader either to be already familiar with functional analysis or to become motivated by the practical examples given here to read a book about this topic. We recall that also from a historical point of view, functional analysis was initially stimulated by the investigation of integral equations.

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Inhaltsangabe1 Introduction.- 1.1 Integral Equations.- 1.2 Basics from Analysis.- 1.2.1 Continuous Functions.- 1.2.2 Lipschitz Continuous Functions.- 1.2.3 Hölder Continuous Functions.- 1.3 Basics from Functional Analysis.- 1.3.1 Banach Spaces.- 1.3.2 Banach Spaces CLIEN(D), CLk(D), ?LIEN (D).- 1.3.3 Banach Spaces L1(D), L2(D), L?(D).- 1.3.4 Dense Subspaces.- 1.3.5 Banach's Fixed Point Theorem.- 1.3.6 Linear Operators.- 1.3.7 Theorem of Uniform Boundedness.- 1.3.8 Compact Sets and Compact Mappings.- 1.3.9 Riesz-Schauder Theory.- 1.3.10 Hilbert Spaces, Orthogonal Complements, Projections.- 1.4 Basics from Numerical Mathematics.- 1.4.1 Interpolation.- 1.4.2 Quadrature.- 1.4.3 Condition Number of a System of Equations.- 2 Volterra Integral Equations.- 2.1 Theory of Volterra Integral Equations of the Second Kind.- 2.1.1 Existence und Uniqueness of the Solution.- 2.1.2 Regularity of the Solution.- 2.2 Numerical Solution by Quadrature Methods.- 2.2.1 Derivation of the Discretisation.- 2.2.2 Error Estimate.- 2.3 Further Numerical Methods.- 2.4 Linear Volterra Integral Equations of Convolution Type.- 2.5 The Volterra Integral Equations of the First Kind.- 3 Theory of Fredholm Integral Equations of the Second Kind.- 3.1 The Fredholm Integral Equation of the Second Kind.- 3.2 Compactness of the Integral Operator K.- 3.2.1 General Considerations.- 3.2.2 The Case X = C(D).- 3.2.3 The Case X = L2(D).- 3.2.4 The Case of an Unbounded Interval I.- 3.3 Finite Approximability of the Integral Operator K.- 3.3.1 Convergence with Respect to the Operator Norm.- 3.3.2 Degenerate Kernels.- 3.4 The Image Space of K.- 3.4.1 Smooth Kernels k(x, y).- 3.4.2 The Image Kf for f?C?(I).- 3.4.3 Kernels with Integrable Singularity.- 3.4.4 Compactness.- 3.4.5 Volterra Integral Equation.- 3.4.6 K as Mapping Defined on L?(D).- 3.5 Solution of the Fredholm Integral Equation of the Second Kind.- 3.5.1 Existence and Uniqueness.- 3.5.2 Regularity.- 4 Numerical Treatment of Fredholm Integral Equations of the Second Kind.- 4.1 General Considerations.- 4.1.1 Notation of the Semidiscrete Problem.- 4.1.2 Consistency and Stability.- 4.1.3 Convergence.- 4.1.4 Stability and Convergence Theorem.- 4.1.5 Error Estimates.- 4.1.6 Condition Numbers.- 4.2 Discretisation by Kernel Approximation.- 4.2.1 Degenerate Kernels.- 4.2.2 Setting Up the System of Equations.- 4.2.3 Kernel Approximation by Interpolation.- 4.2.4 Tensor Approximation of k.- 4.2.5 Examples of Kernel Approximations.- 4.2.6 A Variant of the Kernel Approximation.- 4.2.7 Analysis of the System of Equations.- 4.2.8 Numerical Examples.- 4.3 Projection Methods in General.- 4.3.1 Subspaces.- 4.3.2 Projections.- 4.3.3 Lemmata.- 4.3.4 Discretisation by means of a Projection.- 4.3.5 Convergence Analysis.- 4.3.6 Error Estimate.- 4.4 Collocation Method.- 4.4.1 Definition of the Projection by Interpolation.- 4.4.2 Setting up the System of Linear Equations.- 4.4.3 Examples for Interpolations.- 4.4.4 Condition Number of the System of Equations.- 4.4.5 Numerical Examples.- 4.5 Galerkin Method.- 4.5.1 Subspace, Orthogonal Projection.- 4.5.2 Derivation of the System of Equations.- 4.5.3 Convergence in L2(D) and L?(D).- 4.5.4 Error Estimates.- 4.5.5 Condition Number of the System of Equations.- 4.5.6 Example: Piecewise Constant Functions.- 4.5.7 Example: Piecewise Linear Functions.- 4.5.8 General Analysis of Projection Errors.- 4.5.9 Revisited: Piecewise Linear Functions.- 4.5.10 Numerical Examples.- 4.6 Additional Comments Concerning Projection Methods.- 4.6.1 Regularisation Method.- 4.6.2 Estimates with Respect to Weaker Norms.- 4.6.3 The Iterated Approximation.- 4.6.4 Superconvergence.- 4.6.5 More General Formulations of the Projection Method.- 4.6.6 Numerical Quadrature.- 4.6.7 Product Integration.- 4.7 Discretisation by Quadrature: The Nyström Method.- 4.7.1 Description of the Method.- 4.7.2 Convergence Analysis.- 4.7.3 Stability.- 4.7.4 Consistency Order.- 4.7.5 Condition Number of the System of Equations.- 4.7.6 Regularisation.- 4.7.7 Numer