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|a 10.1007/978-981-15-2910-8
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|a Lin, Zhouchen.
|e author.
|0 (orcid)0000-0003-1493-7569
|1 https://orcid.org/0000-0003-1493-7569
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 245 |
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|a Accelerated Optimization for Machine Learning
|h [electronic resource] :
|b First-Order Algorithms /
|c by Zhouchen Lin, Huan Li, Cong Fang.
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| 250 |
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|a 1st ed. 2020.
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| 264 |
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|a Singapore :
|b Springer Singapore :
|b Imprint: Springer,
|c 2020.
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| 300 |
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|a XXIV, 275 p. 36 illus.
|b online resource.
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|a text
|b txt
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|a 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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|a 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.
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|a Machine learning.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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|a Computer science—Mathematics.
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|a Computer mathematics.
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|a Machine Learning.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21010
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|a Optimization.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M26008
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|a Math Applications in Computer Science.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I17044
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| 650 |
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|a Computational Mathematics and Numerical Analysis.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M1400X
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| 700 |
1 |
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|a Li, Huan.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 700 |
1 |
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|a Fang, Cong.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9789811529092
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|i Printed edition:
|z 9789811529115
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| 950 |
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