Alternating Direction Method of Multipliers for Machine Learning
Produktnummer:
18ee218d32d4624b41a39d3331edd7a8df
| Autor: | Fang, Cong Li, Huan Lin, Zhouchen |
|---|---|
| Themengebiete: | Alternating Direction Method of Multipliers Constrained Optimization First-Order Optimization Lagrange Multiplier Method Operator Splitting |
| Veröffentlichungsdatum: | 16.06.2022 |
| EAN: | 9789811698392 |
| Sprache: | Englisch |
| Seitenzahl: | 263 |
| Produktart: | Gebunden |
| Verlag: | Springer Singapore |
Produktinformationen "Alternating Direction Method of Multipliers for Machine Learning"
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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