Initial Value Methods for Boundary Value Problems. Theory and Application of Invariant Imbedding [Mathematics in Science and Engineering, Volume 100]

Meyer, Gunter H.

ISBN 10: 0124929508 / ISBN 13: 9780124929500

Editore: Academie Press, New York London, 1973

Riguardo questo articolo

Leinen Originalleineneinband mit vergoldetem Rücken- und Deckeltitel. Der Einband ist sauber und gut, der Kopfschnitt ist etwas verstaubt. Die Seiten sind sauber und frisch. Der Schutzumschlag ist etwas berieben mit leichten Gebrauchsspuren. This book develops the numerical and analytical solution of boundary value problems for ordinary differential equations through conversion into initial value problems. The conversion method is based on an implicit shooting method and leads to the so-called invariant imbedding equations. (Klappentext) Sprache / Language: en XII + 220 Seiten. ca. 23,5 x 15,7 cm.

Codice articolo KIE7242

Dati bibliografici

• Titolo: Initial Value Methods for Boundary Value Problems. Theory and Application of Invariant Imbedding [Mathematics in Science and Engineering, Volume 100]
• Editore: Academie Press, New York London
• Data di pubblicazione: 1973
• ISBN 10: 0124929508
• ISBN 13: 9780124929500
• Edizione: 1.Auflage.
• Condizione: Gut
• Cataloghi dei venditori:
  • Naturwissenschaft/Mathematik

Descrizione articolo

Riassunto

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and, methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. This book covers: best operator approximation, non-Lagrange interpolation, generic Karhunen-Loeve transform, generalised low-rank matrix approximation, optimal data compression, and optimal nonlinear filtering.

Contenuti

Preface

Contents

1 Overview

I Methods of Operator Approximation in System Modelling

2 Nonlinear Operator Approximation with Preassigned Accuracy

2.1 Introduction

2.2 Generic formulation of the problem

2.3 Operator approximation in space C([0; 1]):

2.4 Operator approximation in Banach spaces by polynomial operators

2.5 Approximation on compact sets in topological vector spaces

2.6 Approximation on noncompact sets in Hilbert spaces

2.7 Special results for maps into Banach spaces

2.8 Concluding remarks

3 Interpolation of Nonlinear Operators 65

3.1 Introduction

3.2 Lagrange interpolation in Banach spaces

3.3 Weak interpolation of nonlinear operators

3.4 Some related results

3.5 Concluding remarks

4 Realistic Operators and their Approximation

4.1 Introduction

4.2 Formalization of concepts related to description of real-world objects

4.3 Approximation of R¡continuous operators

4.4 Concluding remarks

5 Methods of Best Approximation for Nonlinear Operators

5.1 Introduction

5.2 Best Approximation of nonlinear operators in Banach spaces: Deterministic case

5.3 Estimation of mean and covariance matrix for random vectors

5.4 Best Hadamard-quadratic approximation

5.5 Best polynomial approximation

5.6 Best causal approximation

5.7 Best hybrid approximations

5.8 Concluding remarks

II Optimal Estimation of Random Vectors

6 Computational Methods for Optimal Filtering of Stochastic Signals

6.1 Introduction

6.2 Optimal linear Filtering in Finite dimensional vector spaces

6.3 Optimal linear Filtering in Hilbert spaces

6.4 Optimal causal linear Filtering with piecewise constant memory

6.5 Optimal causal polynomial Filtering with arbitrarily variable memory

6.6 Optimal nonlinear Filtering with no memory constraint

6.7 Concluding remarks

7 Computational Methods for Optimal Compression and
Reconstruction of Random Data

7.1 Introduction

7.2 Standard Principal Component Analysis and Karhunen-Loeeve transform (PCA{KLT)

7.3 Rank-constrained matrix approximations

7.4 Generic PCA{KLT

7.5 Optimal hybrid transform based on Hadamard-quadratic approximation

7.6 Optimal transform formed by a combination of nonlinear operators

7.7 Optimal generalized hybrid transform

7.8 Concluding remarks

Bibliography

Index