Description: Accelerated Optimization for Machine Learning : First-order Algorithms, Paperback by Lin, Zhouchen; Li, Huan; Fang, Cong, ISBN 9811529124, ISBN-13 9789811529122, Like New Used, Free shipping in the US
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, th 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.
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Book Title: Accelerated Optimization for Machine Learning : First-order Algor
Number of Pages: Xxiv, 275 Pages
Language: English
Publication Name: Accelerated Optimization for Machine Learning : First-Order Algorithms
Publisher: Springer
Publication Year: 2021
Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Optimization
Item Weight: 16.3 Oz
Type: Textbook
Subject Area: Computers, Mathematics
Author: Huan Li, Zhouchen Lin, Cong Fang
Item Length: 9.3 in
Item Width: 6.1 in
Format: Trade Paperback
