Accelerated Optimization for Machine Learning: First-Order Algorithms Hardcover – 30 May 2020
By 作者:Zhouchen Lin , Huan Li , Cong Fang (Author)
Pages 页数: 300 pages
Publisher Finelybook 出版社: Springer; 1st ed. 2020 edition (30 May 2020)
Language 语言: English
Book Description to Finelybook sorting
This book on optimization includes forewords By 作者: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 作者: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.
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