Items related to Alternating Direction Method of Multipliers for Machine...
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Zhouchen Lin is a leading expert in the fields of machine learning and optimization. He is currently a professor with the Key Laboratory of Machine Perception (Ministry of Education), School of Artificial Intelligence, Peking University. Prof. Lin served as an area chair many times for prestigious conferences, including CVPR, ICCV, NIPS/NeurIPS, ICML, ICLR, IJCAI and AAAI. He is a Program Co-Chair of ICPR 2022 and a Senior Area Chair of ICML 2022. Prof. Lin is an associate editor of the International Journal of Computer Vision and the Optimization Methods and Software. He is a Fellow of CSIG, IAPR and IEEE.
Huan Li received a doctoral degree in machine learning from Peking University in 2019. He is currently an assistant researcher at the School of Artificial Intelligence, Nankai University. His research interests include optimization and machine learning.
Cong Fang received a doctoral degree in machine learning from Peking University in 2019. He is currently anassistant professor at the School of Artificial Intelligence, Peking University. His research interests include optimization and machine learning.
From the Back 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.
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2022
- ISBN 10 9811698392
- ISBN 13 9789811698392
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages286
Other Popular Editions of the Same Title
Search results for Alternating Direction Method of Multipliers for Machine...
Stock Image
Alternating Direction Method o
Seller: 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. Seller Inventory # 00079463841
Quantity: 1 available
Stock Image
Alternating Direction Method of Multipliers for Machine Learning
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Apr0412070093774
Quantity: Over 20 available
Stock Image
Alternating Direction Method of Multipliers for Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9789811698392_new
Quantity: Over 20 available
Stock Image
Alternating Direction Method of Multipliers for Machine Learning
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9789811698392
Quantity: Over 20 available
Seller Image
Alternating Direction Method of Multipliers for Machine Learning
Published by
Springer Nature Singapore Jun 2022, 2022
ISBN 10: 9811698392
ISBN 13: 9789811698392
New
Hardcover
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. Seller Inventory # 9789811698392
Quantity: 2 available
Seller Image
Alternating Direction Method of Multipliers for Machine Learning
Published by
Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811698392
ISBN 13: 9789811698392
New
Hardcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
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. Seller Inventory # 541927504
Quantity: Over 20 available
Stock Image
Alternating Direction Method of Multipliers for Machine Learning
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP. Seller Inventory # 26396346621
Quantity: 4 available
Seller Image
Alternating Direction Method of Multipliers for Machine Learning
Published by
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811698392
ISBN 13: 9789811698392
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
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. Seller Inventory # 9789811698392
Quantity: 1 available
Stock Image
Alternating Direction Method of Multipliers for Machine Learning
Print on DemandSeller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # 401111842
Quantity: 4 available
Seller Image
Alternating Direction Method of Multipliers for Machine Learning
Published by
Springer Nature Singapore, Springer Nature Singapore Jun 2022, 2022
ISBN 10: 9811698392
ISBN 13: 9789811698392
New
Hardcover
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. Seller Inventory # 9789811698392
Quantity: 2 available