Mohlo by se vám také líbit

Více informací o knize Alternating Direction Method of Multipliers for Machine Learning

• Parametry knihy
• Anotace
• Oblíbené z jiného soudku

Nákupem získáte 480 bodů

Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Alternating Direction Method of Multipliers for Machine Learning
• Autor: Zhouchen Lin, Huan Li, Cong Fang
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 263
• EAN: 9789811698392
• ID: 38574039
• Nakladatelství: Springer, Berlin
• Hmotnost: 600 g
• Rozměry: 235 × 155 × 21 mm
• Rok vydání: 2022

Oblíbené z jiného soudku

---

---

---