Accelerated Optimization for Machine Learning
First-Order Algorithms
Häftad, Engelska, 2021
Av Zhouchen Lin, Huan Li, Cong Fang
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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.
Produktinformation
- Utgivningsdatum2021-05-30
- Mått155 x 235 x 16 mm
- Vikt422 g
- FormatHäftad
- SpråkEngelska
- Antal sidor275
- Upplaga2020
- FörlagSpringer Verlag, Singapore
- ISBN9789811529122