Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Foundations of Smooth Optimization
- 3 Descent Methods
- 4 Gradient Methods Using Momentum
- 5 Stochastic Gradient
- 6 Coordinate Descent
- 7 First-Order Methods for Constrained Optimization
- 8 Nonsmooth Functions and Subgradients
- 9 Nonsmooth Optimization Methods
- 10 Duality and Algorithms
- 11 Differentiation and Adjoints
- Appendix
- Bibliography
- Index
1 - Introduction
Published online by Cambridge University Press: 31 March 2022
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Foundations of Smooth Optimization
- 3 Descent Methods
- 4 Gradient Methods Using Momentum
- 5 Stochastic Gradient
- 6 Coordinate Descent
- 7 First-Order Methods for Constrained Optimization
- 8 Nonsmooth Functions and Subgradients
- 9 Nonsmooth Optimization Methods
- 10 Duality and Algorithms
- 11 Differentiation and Adjoints
- Appendix
- Bibliography
- Index
HTML view is not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
Summary
In this introductory chapter, we outline the ways in which various problems in data analysis can be formulated as optimization problems. Specifically, we discuss least squares problems, problems in matrix optimization (particularly those involving low-rank matrices), linear and kernel support vector machines, binary and multiclass logistic regression, and deep learning. We also outline the scope of the remainder of the book.
Keywords
- Type
- Chapter
- Information
- Optimization for Data Analysis , pp. 1 - 14Publisher: Cambridge University PressPrint publication year: 2022
You have
Access