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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Accelerated Optimization for Machine Learning
• Subtitle: First-Order Algorithms
• Author: Zhouchen Lin, Huan Li, Cong Fang
• Language: English
• Binding: Hardback
• Number of pages: 275
• EAN: 9789811529092
• ID: 25392337
• Publisher: Springer Verlag, Singapore
• Weight: 640 g
• Dimensions: 235 × 155 × 22 mm
• Date of publishing: 30. May 2020

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