Non-Convex Optimization for Machine Learning

ISBN-13: 9781680833683 / Angielski / Miękka / 2017 / 218 str.

Prateek Jain; Purushottam Kar

Opis

Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems.

Spis treści

• Preface
• Mathematical Notation
• Part I: Introduction and Basic Tools
• 1. Introduction
• 2. Mathematical ToolsPart II: Non-convex Optimization Primitives
• 3. Non-Convex Projected Gradient Descent
• 4. Alternating Minimization
• 5. The EM Algorithm
• 6. Stochastic Optimization TechniquesPart III: Applications
• 7. Sparse Recovery
• 8. Low-rank Matrix Recovery
• 9. Robust Linear Regression
• 10. Phase Retrieval
• References