Accelerated Optimization for Machine Learning

First-Order Algorithms

von Zhouchen Lin, Huan Li, Cong Fang

275 Seiten, Hardcover

Kurzbeschreibung des Verlags

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.

• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Maschinelles Lernen
• ISBN: 9789811529092
• Sprache: Englisch
• Erscheinungsdatum: 30.05.2020
• Größe: 23.5 x 15.5 cm
• Verlag: Springer Singapore