Alternating Direction Method of Multipliers for Machine Learning - Couverture souple

Lin, Zhouchen; Li, Huan; Fang, Cong

9789811698422: Alternating Direction Method of Multipliers for Machine Learning

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ISBN 10 : 9811698422 ISBN 13 : 9789811698422

Editeur : Springer, 2023

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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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� propos de l?auteur

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.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2023
Langue: anglais
ISBN 10: 9811698422
ISBN 13: 9789811698422
Reliure: Broch�
Nombre de pages: 288
Coordonn�es du fabricant: non disponible
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Alternating Direction Method of Multipliers for Machine Learning

Lin, Zhouchen|Li, Huan|Fang, Cong

Edit� par Springer, Berlin|Springer Nature Singapore|Springer, 2023

ISBN 10 : 9811698422 ISBN 13 : 9789811698422

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Etat : 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. N� de r�f. du vendeur 877457631

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Alternating Direction Method of Multipliers for Machine Learning

Lin, Zhouchen; Li, Huan; Fang, Cong

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ISBN 10 : 9811698422 ISBN 13 : 9789811698422

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Alternating Direction Method of Multipliers for Machine Learning

Zhouchen Lin

Edit� par Springer Nature Singapore Jun 2023, 2023

ISBN 10 : 9811698422 ISBN 13 : 9789811698422

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9789811698422

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Alternating Direction Method of Multipliers for Machine Learning

Zhouchen Lin

Edit� par Springer Nature Singapore, Springer Nature Singapore Jun 2023, 2023

ISBN 10 : 9811698422 ISBN 13 : 9789811698422

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9789811698422

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Alternating Direction Method of Multipliers for Machine Learning

Zhouchen Lin

Edit� par Springer Nature Singapore, Springer Nature Singapore, 2023

ISBN 10 : 9811698422 ISBN 13 : 9789811698422

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9789811698422

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