Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Executive Summary
- 19 Basic Convex Optimization
- 20 Snippets of Linear Programming
- 21 Duality Theory and Practice
- 22 Semidefinite Programming in Action
- 23 Instances of Nonconvex Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
20 - Snippets of Linear Programming
from Part Four - Optimization
Published online by Cambridge University Press: 21 April 2022
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Executive Summary
- 19 Basic Convex Optimization
- 20 Snippets of Linear Programming
- 21 Duality Theory and Practice
- 22 Semidefinite Programming in Action
- 23 Instances of Nonconvex Optimization
- Part Five Neural Networks
- Appendices
- References
- Index
Summary
This chapter focuses exclusively on linear programs. It first describes the simplex algorithm used to solve linear programs in standard form. It then presents a series of examples illustrating the usefulness of slack variables to transform some geometric problems into linear programs.
- Type
- Chapter
- Information
- Mathematical Pictures at a Data Science Exhibition , pp. 169 - 176Publisher: Cambridge University PressPrint publication year: 2022