Description
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.
Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Table of Contents
Chapter 1. Introduction.- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization.- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization.- Chapter 4. Accelerated Algorithms for Nonconvex Optimization.- Chapter 5. Accelerated Stochastic Algorithms.- Chapter 6. Accelerated Paralleling Algorithms.- Chapter 7. Conclusions.-
