Alternating Direction Method of Multipliers for Machine Learning / by Zhouchen Lin, Huan Li, Cong Fang
データ種別 | 電子ブック |
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版 | 1st ed. 2022. |
出版者 | (Singapore : Springer Nature Singapore : Imprint: Springer) |
出版年 | 2022 |
大きさ | XXIII, 263 p. 1 illus : online resource |
著者標目 | *Lin, Zhouchen author Li, Huan author Fang, Cong author SpringerLink (Online service) |
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一般注記 | Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time HTTP:URL=https://doi.org/10.1007/978-981-16-9840-8 |
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件 名 | LCSH:Machine learning LCSH:Mathematical optimization LCSH:Computer science—Mathematics LCSH:Mathematics—Data processing FREE:Machine Learning FREE:Optimization FREE:Mathematical Applications in Computer Science FREE:Computational Mathematics and Numerical Analysis |
分 類 | LCC:Q325.5-.7 DC23:006.31 |
書誌ID | EB00002172 |
ISBN | 9789811698408 |
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