Alternating Direction Method of Multipliers for Machine Learning
暫譯: 機器學習的交替方向乘子法
Lin, Zhouchen, Li, Huan, Fang, Cong
- 出版商: Springer
- 出版日期: 2022-06-16
- 售價: $6,400
- 貴賓價: 9.5 折 $6,080
- 語言: 英文
- 頁數: 253
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9811698392
- ISBN-13: 9789811698392
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
機器學習在解決其學習模型時，重度依賴優化算法。約束問題構成了一種主要的優化問題類型，而交替方向乘子法（ADMM）是一種常用的算法，用於解決約束問題，特別是線性約束問題。本書由機器學習和優化領域的專家撰寫，是第一本提供關於ADMM在各種情境下的最先進回顧的書籍，包括確定性和凸優化、非凸優化、隨機優化以及分散式優化。書中提供了豐富的思想、理論和證明，內容更新且自成體系。對於尋求相對通用的約束問題算法的使用者來說，這是一本極佳的參考書。研究生或研究人員可以閱讀本書，以在短時間內掌握ADMM在機器學習中的前沿知識。
作者簡介
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 an assistant professor at the School of Artificial Intelligence, Peking University. His research interests include optimization and machine learning.
作者簡介(中文翻譯)
周晨林是機器學習和優化領域的領先專家。他目前是北京大學人工智慧學院機器感知重點實驗室（教育部）的教授。林教授曾多次擔任多個知名會議的區域主席，包括 CVPR、ICCV、NIPS/NeurIPS、ICML、ICLR、IJCAI 和 AAAI。他是 ICPR 2022 的程式共同主席，以及 ICML 2022 的高級區域主席。林教授是《國際計算機視覺期刊》和《優化方法與軟體》的副編輯。他是 CSIG、IAPR 和 IEEE 的會士。
李煥於 2019 年在北京大學獲得機器學習博士學位。他目前是南開大學人工智慧學院的助理研究員。他的研究興趣包括優化和機器學習。
方聰於 2019 年在北京大學獲得機器學習博士學位。他目前是北京大學人工智慧學院的助理教授。他的研究興趣包括優化和機器學習。
