Idioma: Inglés
Publicado por Society for Industrial and Applied Mathematics,U.S., US, 2025
ISBN 10: 1611978556 ISBN 13: 9781611978551
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 78,11
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: New. Conditional Gradient Methods: From Core Principles to AI Applications offers a definitive and modern treatment of one of the most elegant and versatile algorithmic families in optimization: the Frank-Wolfe method and its many variants. Originally proposed in the 1950s, these projection-free techniques have seen a powerful resurgence, now playing a central role in machine learning, signal processing, and large-scale data science. This comprehensive monograph unites deep theoretical insights with practical considerations, guiding readers through the foundations of constrained optimization and into cutting-edge territory, including stochastic, online, and distributed settings. With a clear narrative, rigorous proofs, and illuminating illustrations, the book demystifies adaptive variants, away-steps, and the nuances of dealing with structured convex sets. A FrankWolfe.jl Julia package that implements most of the algorithms in the book is available on a supplementary website.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 69,44
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 198 pages. 10.00x7.00x0.30 inches. In Stock.
Idioma: Inglés
Publicado por Society for Industrial and Applied Mathematics,U.S., US, 2025
ISBN 10: 1611978556 ISBN 13: 9781611978551
Librería: Rarewaves.com UK, London, Reino Unido
EUR 70,63
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: New. Conditional Gradient Methods: From Core Principles to AI Applications offers a definitive and modern treatment of one of the most elegant and versatile algorithmic families in optimization: the Frank-Wolfe method and its many variants. Originally proposed in the 1950s, these projection-free techniques have seen a powerful resurgence, now playing a central role in machine learning, signal processing, and large-scale data science. This comprehensive monograph unites deep theoretical insights with practical considerations, guiding readers through the foundations of constrained optimization and into cutting-edge territory, including stochastic, online, and distributed settings. With a clear narrative, rigorous proofs, and illuminating illustrations, the book demystifies adaptive variants, away-steps, and the nuances of dealing with structured convex sets. A FrankWolfe.jl Julia package that implements most of the algorithms in the book is available on a supplementary website.