Isbn: 9781611978551 - conditional gradient methods: from core principles to ai applications (16 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Paperback. 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. …

Paperback. Condición: Brand New. 198 pages. 10.00x7.00x0.30 inches. In Stock.

Condición: As New. Unread book in perfect condition.

Condición: New. 2025. paperback. . . . . .

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. 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 FrankWolfe 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. Blends solid theoretical foundations with practical insight, the work demystifies projection-free optimization through the FrankWolfe method and its adaptive variants. This book tackles constrained challenges in machine learning, signal processing, and large-scale data, supported by rigorous proofs and clear illustrations. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Condición: New.

Paperback. Condición: new. Paperback. 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 FrankWolfe 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. Blends solid theoretical foundations with practical insight, the work demystifies projection-free optimization through the FrankWolfe method and its adaptive variants. This book tackles constrained challenges in machine learning, signal processing, and large-scale data, supported by rigorous proofs and clear illustrations. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. 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. …

Paperback. Condición: new. Paperback. 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 FrankWolfe 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. Blends solid theoretical foundations with practical insight, the work demystifies projection-free optimization through the FrankWolfe method and its adaptive variants. This book tackles constrained challenges in machine learning, signal processing, and large-scale data, supported by rigorous proofs and clear illustrations. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…