Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- 24 Key Points
- 25 Problem Setting
- 26 Step-Size Selection – a Case Study
- 27 Controlling Optimisation by Probabilistic Estimates
- 28 First- and Second-Order Methods
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
27 - Controlling Optimisation by Probabilistic Estimates
from IV - Local Optimisation
Published online by Cambridge University Press: 01 June 2022
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- 24 Key Points
- 25 Problem Setting
- 26 Step-Size Selection – a Case Study
- 27 Controlling Optimisation by Probabilistic Estimates
- 28 First- and Second-Order Methods
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
Summary
- Type
- Chapter
- Information
- Probabilistic NumericsComputation as Machine Learning, pp. 221 - 228Publisher: Cambridge University PressPrint publication year: 2022