Accelerated Optimization for Machine Learning: First-Order Algorithms

di Zhouchen Lin (Autore)

Huan Li (Autore)

Cong Fang (Autore)

Springer Verlag, Singapore, 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 asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

• Autore: Zhouchen Lin, Huan Li, Cong Fang
• Editore: Springer Verlag, Singapore
• Anno: 2020
• Rilegatura: Hardback
• Pagine: 275 p.
• Dimensioni: 235 x 155 mm
• EAN: 9789811529092

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