SPRI | Accelerated Optimization For Machine Learning: First-Order Algorithms (2020 EN)

Author: Zhouchen Lin, Huan Li, Cong Fang
Full Title: Accelerated Optimization For Machine Learning: First-Order Algorithms
Publisher: Springer; 1st ed. 2020 Edition (May 30, 2020)
Year: 2020
ISBN-13: 9789811529108 (978-981-15-2910-8), 9789811529092 (978-981-15-2909-2)
ISBN-10: 9811529108, 9811529094
Pages: 275
Language: English
Genre: Educational: Artificial Intelligence
File type: EPUB (True), PDF (True)
Quality: 10/10
Price: 114.39 €


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.

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