Alternating Direction Method of Multipliers for Machine Learning

Released on by Zhouchen Lin
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  • Alternating Direction Method of Multipliers for Machine Learning Book Detail

  • Author : Zhouchen Lin
  • Release Date : 2022-06-15
  • Publisher : Springer Nature
  • Genre : Computers
  • Pages : 274
  • ISBN 13 : 9811698406
  • File Size : 74,74 MB
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Alternating Direction Method of Multipliers for Machine Learning by Zhouchen Lin PDF Summary

Book Description: 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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