| 期刊全称 | Accelerated Optimization for Machine Learning | | 期刊简称 | First-Order Algorith | | 影响因子2023 | Zhouchen Lin,Huan Li,Cong Fang | | 视频video | | | 发行地址 | The first monograph on accelerated first-order optimization algorithms used in machine learning.Includes forewords by Michael I. Jordan, Zongben Xu, and Zhi-Quan Luo, and written by experts on machine | | 图书封面 |  | | 影响因子 | .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 asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.. | | Pindex | Book 2020 |
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发表于 2025-3-21 17:05:55
