Authors:
- 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 learning and optimization
- Is comprehensive, up-to-date, and self-contained, making it is easy for beginners to grasp the frontiers of optimization in machine learning
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Key Lab. of Machine Perception School of EECS, Peking University, Beijing, China
Zhouchen Lin
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College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Huan Li
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School of Engineering and Applied Science, Princeton University, Princeton, USA
Cong Fang
About the authors
Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.
Huan Li received his Ph.D. degree in machine learning from Peking University in 2019. He is currently an Assistant Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. His current research interests include optimization and machine learning.
Cong Fang received his Ph.D. degree from Peking University in 2019. He is currently a Postdoctoral Researcher at Princeton University. His research interests include machine learning and optimization.
Bibliographic Information
Book Title: Accelerated Optimization for Machine Learning
Book Subtitle: First-Order Algorithms
Authors: Zhouchen Lin, Huan Li, Cong Fang
DOI: https://doi.org/10.1007/978-981-15-2910-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-2909-2Published: 30 May 2020
Softcover ISBN: 978-981-15-2912-2Published: 30 May 2021
eBook ISBN: 978-981-15-2910-8Published: 29 May 2020
Edition Number: 1
Number of Pages: XXIV, 275
Number of Illustrations: 36 b/w illustrations
Topics: Machine Learning, Optimization, Math Applications in Computer Science, Computational Mathematics and Numerical Analysis