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Alternating Direction Method Multipliers by Lin Zhouchen (26 results)
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Alternating Direction Method of Multipliers for Machine Learning
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Condition: New.
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Alternating Direction Method of Multipliers for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Alternating Direction Method of Multipliers for Machine Learning
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Alternating Direction Method of Multipliers for Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Alternating Direction Method of Multipliers for Machine Learning
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Alternating Direction Method of Multipliers for Machine Learning | Zhouchen Lin (u. a.) | Taschenbuch | xxiii | Englisch | 2023 | Springer | EAN 9789811698422 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Jun 2022, 2022
ISBN 10: 9811698392 ISBN 13: 9789811698392
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Paperback. Condition: Brand New. 286 pages. 9.25x6.10x0.60 inches. In Stock.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer-Nature New York Inc, 2022
ISBN 10: 9811698392 ISBN 13: 9789811698392
Hardcover. Condition: Brand New. 286 pages. 9.25x6.10x0.91 inches. In Stock.
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer Nature Singapore Jun 2023, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 288 pp. Englisch.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer Nature Singapore Jun 2022, 2022
ISBN 10: 9811698392 ISBN 13: 9789811698392
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 288 pp. Englisch.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 solv.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811698392 ISBN 13: 9789811698392
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 solv.
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Condition: New. Print on Demand.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Condition: New. PRINT ON DEMAND.
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Alternating Direction Method of Multipliers for Machine Learning
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Jun 2023, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch.
