Alternating Direction Method of Multipliers for Machine Learning / by Zhouchen Lin, Huan Li, Cong Fang

By:

Lin, Zhouchen [VerfasserIn]

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer eBook CollectionPublisher: Singapore : Springer Nature Singapore, 2022Publisher: Singapore : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 Online-Ressource(XXIII, 263 p. 1 illus.)ISBN:

9789811698408

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Additional physical formats: 9789811698392 | 9789811698415 | 9789811698422 | Erscheint auch als: 9789811698392 Druck-Ausgabe | Erscheint auch als: 9789811698415 Druck-Ausgabe | Erscheint auch als: 9789811698422 Druck-Ausgabe | Erscheint auch als: Alternating direction method of multipliers for machine learning. Druck-Ausgabe Singapore : Springer Nature, 2022. xxiii, 263 SeitenDOI: DOI: 10.1007/978-981-16-9840-8Online resources:

Summary: 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.Summary: 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.PPN: PPN: 180734214XPackage identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS

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