Accelerated optimization for machine learning : first-order algorithms / / Zhouchen Lin, Huan Li, Cong Fang
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| Autore: |
Lin Zhouchen
|
| Titolo: |
Accelerated optimization for machine learning : first-order algorithms / / Zhouchen Lin, Huan Li, Cong Fang
|
| Pubblicazione: | Singapore : , : Springer, , [2020] |
| ©2020 | |
| Descrizione fisica: | 1 online resource (286 pages) |
| Disciplina: | 006.31 |
| Soggetto topico: | Machine learning - Mathematics |
| Mathematical optimization | |
| Computer science - Mathematics | |
| Machine Learning | |
| Optimization | |
| Math Applications in Computer Science | |
| Computational Mathematics and Numerical Analysis | |
| Persona (resp. second.): | LiHuan |
| FangCong | |
| Nota di bibliografia: | Includes bibliographical references and index. |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Accelerated Optimization for Machine Learning |
| ISBN: | 981-15-2910-8 |
| 9789811529108 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910409667103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |