Alternating Direction Method of Multipliers for Machine Learning

机器学习乘法器的交替方向法

工业工程学

原   价:
1322.5
售   价:
1058.00
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平台大促 低至8折优惠
作      者
出  版 社
出版时间
2022年06月15日
装      帧
精装
ISBN
9789811698392
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页      码
253
语      种
英文
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图书简介
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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