Accelerated Optimization for Machine Learning First-Order Algorithms /
Lin, Zhouchen.
Accelerated Optimization for Machine Learning First-Order Algorithms / [electronic resource] : by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2020. - XXIV, 275 p. 36 illus. online resource.
Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.-.
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.
9789811529108
10.1007/978-981-15-2910-8 doi
Machine learning.
Mathematical optimization.
Computer science--Mathematics.
Mathematics--Data processing.
Machine Learning.
Optimization.
Mathematical Applications in Computer Science.
Computational Mathematics and Numerical Analysis.
Q325.5-.7
006.31
Accelerated Optimization for Machine Learning First-Order Algorithms / [electronic resource] : by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2020. - XXIV, 275 p. 36 illus. online resource.
Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.-.
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.
9789811529108
10.1007/978-981-15-2910-8 doi
Machine learning.
Mathematical optimization.
Computer science--Mathematics.
Mathematics--Data processing.
Machine Learning.
Optimization.
Mathematical Applications in Computer Science.
Computational Mathematics and Numerical Analysis.
Q325.5-.7
006.31