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Accelerated Optimization for Machine Learning
First-Order Algorithms
Zhouchen Lin, Huan Li, Cong Fang
Livre broché | Anglais
167,95 €
+ 335 points
Format
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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 275
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9789811529122
- Date de parution :
- 30-05-21
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 156 mm x 234 mm
- Poids :
- 421 g
