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Record Nr. |
UNISA996465342403316 |
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Autore |
Lin Zhouchen |
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Titolo |
Accelerated optimization for machine learning : first-order algorithms / / Zhouchen Lin, Huan Li, Cong Fang |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2020] |
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©2020 |
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ISBN |
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981-15-2910-8 |
9789811529108 |
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Descrizione fisica |
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1 online resource (286 pages) |
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Disciplina |
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Soggetti |
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Machine learning - Mathematics |
Mathematical optimization |
Computer mathematics |
Machine Learning |
Optimization |
Math Applications in Computer Science |
Computational Mathematics and Numerical Analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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