Accelerated Optimization for Machine Learning: First-Order Algorithms 2020 Edition Contributor(s): Lin, Zhouchen (Author), Li, Huan (Author), Fang, Cong (Author) |
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ISBN: 9811529094 ISBN-13: 9789811529092 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: May 2020 |
Additional Information |
BISAC Categories: - Computers | Intelligence (ai) & Semantics - Mathematics | Applied - Computers | Data Processing |
Physical Information: 0.81" H x 6.14" W x 9.21" (1.41 lbs) 275 pages |
Descriptions, Reviews, Etc. |
Publisher 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 as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. |