Description |
1 online resource |
Contents |
Introduction -- Accelerated algorithms for unconstrained convex optimization -- Accelerated algorithms for constrained convex optimization -- Accelerated algorithms for nonconvex optimization -- Accelerated stochastic algorithms -- Accelerated parallel algorithms -- Conclusions |
Summary |
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. -- Provided by publisher |
Bibliography |
Includes bibliographical references and index |
Subject |
Machine learning -- Mathematics
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Mathematical optimization.
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Optimization.
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Maths for computer scientists.
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Numerical analysis.
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Machine learning.
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Mathematics -- Applied.
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Computers -- Data Processing.
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Mathematics -- Counting & Numeration.
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Computers -- Intelligence (AI) & Semantics.
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Mathematical optimization
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Form |
Electronic book
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Author |
Li, Huan
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Fang, Cong
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ISBN |
9789811529108 |
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9811529108 |
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