Accelerated Optimization for Machine Learning - First-Order Algorithms (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	May 2021
First published:	2020
Authors:	Zhouchen Lin • Huan Li • Cong Fang
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	275
Edition:	1st ed. 2020
ISBN-13:	978-981-15-2912-2
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Optimization > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	981-15-2912-4
Barcode:	9789811529122

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