Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- 35 Key Points
- 36 Introduction
- 37 Classical ODE Solvers as Regression Methods
- 38 ODE Filters and Smoothers
- 39 Theory of ODE Filters and Smoothers
- 40 Perturbative Solvers
- 41 Further Topics
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
40 - Perturbative Solvers
from VI - Solving Ordinary Differential Equations
Published online by Cambridge University Press: 01 June 2022
Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- 35 Key Points
- 36 Introduction
- 37 Classical ODE Solvers as Regression Methods
- 38 ODE Filters and Smoothers
- 39 Theory of ODE Filters and Smoothers
- 40 Perturbative Solvers
- 41 Further Topics
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Probabilistic NumericsComputation as Machine Learning, pp. 331 - 338Publisher: Cambridge University PressPrint publication year: 2022