Alternating Direction Method of Multipliers for Machine Learning Hardcover - 2022
by Zhouchen Lin; Huan Li; Cong Fang
From the rear cover
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.
Details
- Title Alternating Direction Method of Multipliers for Machine Learning
- Author Zhouchen Lin; Huan Li; Cong Fang
- Binding Hardcover
- Pages 263
- Volumes 1
- Language ENG
- Publisher Springer
- Date 2022-06-16
- Illustrated Yes
- Features Illustrated
- ISBN 9789811698392 / 9811698392
- Weight 1.28 lbs (0.58 kg)
- Dimensions 9.21 x 6.14 x 0.69 in (23.39 x 15.60 x 1.75 cm)
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Alternating Direction Method of Multipliers for Machine Learning
by Zhouchen Lin
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- Hardcover
- ISBN 10 / ISBN 13
- 9789811698392 / 9811698392
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Alternating Direction Method of Multipliers for Machine Learning
by Lin, Zhouchen/ Li, Huan/ Fang, Cong
- New
- Hardcover
- Condition
- New
- Binding
- Hardcover
- ISBN 10 / ISBN 13
- 9789811698392 / 9811698392
- Quantity Available
- 1
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Exeter, Devon, United Kingdom
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A$360.52A$19.46 shipping to USA