Accelerated Optimization for Machine Learning: First-Order Algorithms Paperback - 2021
by Zhouchen Lin; Huan Li; Cong Fang
From the rear cover
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 as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Details
- Title Accelerated Optimization for Machine Learning: First-Order Algorithms
- Author Zhouchen Lin; Huan Li; Cong Fang
- Binding Paperback
- Pages 275
- Volumes 1
- Language ENG
- Publisher Springer
- Date 2021-05-30
- Illustrated Yes
- ISBN 9789811529122 / 9811529124
- Weight 0.93 lbs (0.42 kg)
- Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
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