Alternating Direction Method of Multipliers for Machine Learning - Couverture rigide

Lin, Zhouchen; Li, Huan; Fang, Cong

 
9789811698392: Alternating Direction Method of Multipliers for Machine Learning

Synopsis

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.

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À 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 référence à une autre édition de ce titre.

Autres éditions populaires du même titre

9789811698422: Alternating Direction Method of Multipliers for Machine Learning

Edition présentée

ISBN 10 :  9811698422 ISBN 13 :  9789811698422
Editeur : Springer, 2023
Couverture souple