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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
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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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002784	Computer Scinece R0 2005-6,2022-3				9789811698408

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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
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