Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- 14 Key Points
- 15 Required Background
- 16 Introduction
- 17 Evaluation Strategies
- 18 A Review of Some Classic Solvers
- 19 Probabilistic Linear Solvers: Algorithmic Scaffold
- 20 Computational Constraints
- 21 Uncertainty Calibration
- 22 Proofs
- 23 Summary of Part III
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
18 - A Review of Some Classic Solvers
from III - Linear Algebra
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
- 14 Key Points
- 15 Required Background
- 16 Introduction
- 17 Evaluation Strategies
- 18 A Review of Some Classic Solvers
- 19 Probabilistic Linear Solvers: Algorithmic Scaffold
- 20 Computational Constraints
- 21 Uncertainty Calibration
- 22 Proofs
- 23 Summary of Part III
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
Summary
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- Probabilistic NumericsComputation as Machine Learning, pp. 143 - 148Publisher: Cambridge University PressPrint publication year: 2022