Zhouchen Lin

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.

Alternating Direction Method of Multipliers for Machine Learning

Tieniu Tan

Jian Yang

Xilin Chen

Liang Wang

Nanning Zheng

Guangming Shi

Yanning Zhang

The three-volume set LNCS 11857, 11858, and 11859 constitutes the refereed proceedings of the Second Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, held in Xi’an, China, in November 2019. The 165 revised full papers presented were carefully reviewed and selected from 412 submissions. The papers have been organized in the following topical sections: Part I: Object Detection, Tracking and Recognition, Part II: Image/Video Processing and Analysis, Part III: Data Analysis and Optimization.

Second Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part I

Pattern Recognition and Computer Vision

Second Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part III

Second Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part II

Zhouchen Lin

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