Skip to content

Alternating Direction Method of Multipliers for Machine Learning
Stock Photo: Cover May Be Different

Alternating Direction Method of Multipliers for Machine Learning Hardcover - 2022

by Zhouchen Lin; Huan Li; Cong Fang


From the rear cover

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.

Details

  • Title Alternating Direction Method of Multipliers for Machine Learning
  • Author Zhouchen Lin; Huan Li; Cong Fang
  • Binding Hardcover
  • Pages 263
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Date 2022-06-16
  • Illustrated Yes
  • Features Illustrated
  • ISBN 9789811698392 / 9811698392
  • Weight 1.28 lbs (0.58 kg)
  • Dimensions 9.21 x 6.14 x 0.69 in (23.39 x 15.60 x 1.75 cm)

About the author

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.

Back to Top

More Copies for Sale

Alternating Direction Method of Multipliers for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning

by Zhouchen Lin

  • New
  • Hardcover
Condition
New
Binding
Hardcover
ISBN 10 / ISBN 13
9789811698392 / 9811698392
Quantity Available
395
Seller
Uxbridge, Greater London, United Kingdom
Seller rating:
This seller has earned a 5 of 5 Stars rating from Biblio customers.
Item Price
A$238.43
A$15.45 shipping to USA

Show Details

Description:
Hard Cover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; The Alternating Direction Method of Multipliers for Machine Learning.
Item Price
A$238.43
A$15.45 shipping to USA
Alternating Direction Method of Multipliers for Machine Learning
Stock Photo: Cover May Be Different

Alternating Direction Method of Multipliers for Machine Learning

by Lin, Zhouchen/ Li, Huan/ Fang, Cong

  • New
  • Hardcover
Condition
New
Binding
Hardcover
ISBN 10 / ISBN 13
9789811698392 / 9811698392
Quantity Available
1
Seller
Exeter, Devon, United Kingdom
Seller rating:
This seller has earned a 4 of 5 Stars rating from Biblio customers.
Item Price
A$358.22
A$19.33 shipping to USA

Show Details

Description:
Springer-Nature New York Inc, 2022. Hardcover. New. 286 pages. 9.25x6.10x0.91 inches.
Item Price
A$358.22
A$19.33 shipping to USA