Amin Karbasi (8 results)
Conditional Gradient Methods
Gábor Braun; Alejandro Carderera; Cyrille W. Combettes; Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Sebastian Pokutta
Language: English
Published by SIAM - Society for Industrial and Applied Mathematics, 2025
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contact seller5-star sellerCondition: New
US$ 83.54
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Condition: New.
Language: English
Published by Society for Industrial and Applied Mathematics,U.S., US, 2025
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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US$ 86.19
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Paperback. Condition: 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. …
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 80.85
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Paperback. Condition: Brand New. 198 pages. 10.00x7.00x0.30 inches. In Stock.
Conditional Gradient Methods
Gábor Braun; Alejandro Carderera; Cyrille W. Combettes; Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Sebastian Pokutta
Language: English
Published by SIAM - Society for Industrial and Applied Mathematics, 2025
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contact seller5-star sellerCondition: Used - As new
US$ 90.52
US$ 2.64 shippingShips within U.S.A.Quantity: 9 available
Condition: As New. Unread book in perfect condition.
Conditional Gradient Methods
Gábor Braun; Alejandro Carderera; Cyrille W. Combettes; Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Sebastian Pokutta
Language: English
Published by SIAM - Society for Industrial and Applied Mathematics, 2025
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: New
US$ 81.48
US$ 20.22 shippingShips from United Kingdom to U.S.A.Quantity: 10 available
Condition: New.
Conditional Gradient Methods
Gábor Braun; Alejandro Carderera; Cyrille W. Combettes; Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Sebastian Pokutta
Language: English
Published by SIAM - Society for Industrial and Applied Mathematics, 2025
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: Used - As new
US$ 94.60
US$ 20.22 shippingShips from United Kingdom to U.S.A.Quantity: 9 available
Condition: As New. Unread book in perfect condition.
Conditional Gradient Methods From Core Principles to AI Applications
Gábor Braun|Alejandro Carderera|Cyrille W. Combettes|Hamed Hassani|Amin Karbasi
Language: English
Published by SIAM - Society for Industrial and Applied Mathematics, 2025
- Softcover
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
US$ 92.86
US$ 55.04 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New.
Language: English
Published by Society for Industrial and Applied Mathematics,U.S., US, 2025
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
US$ 85.61
US$ 87.60 shippingShips from United Kingdom to U.S.A.Quantity: 6 available
Paperback. Condition: 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. …
