Alternating Direction Method of Multipliers for Machine Learning
- Publisher's listprice EUR 149.79
-
62 125 Ft (59 167 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 12 425 Ft off)
- Discounted price 49 700 Ft (47 334 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
62 125 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1st ed. 2022
- Publisher Springer Nature Singapore
- Date of Publication 16 June 2022
- Number of Volumes 1 pieces, Book
- ISBN 9789811698392
- Binding Hardback
- No. of pages263 pages
- Size 235x155 mm
- Weight 600 g
- Language English
- Illustrations XXIII, 263 p. 1 illus. Illustrations, black & white 271
Categories
Long 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.
MoreTable of Contents:
Chapter 1. Introduction.- Chapter 2. Derivations of ADMM.- Chapter 3. ADMM for Deterministic and Convex Optimization.- Chapter 4. ADMM for Nonconvex Optimization.- Chapter 5. ADMM for Stochastic Optimization.- Chapter 6. ADMM for Distributed Optimization.- Chapter 7. Practical Issues and Conclusions.
More
