Conditional gradient methods: from core principles to AI applications

By:

Braun, Gabor

Contributor(s):

Series: MOS-SIAM Series on OptimizationPublication details: Philadelphia: Society for Industrial and Applied Mathematics (SIAM), 2025.Description: ix, 195p.: col., ill.; pbk.: 25 cmISBN:

9781611978551

Subject(s):

DDC classification:

519.6 BRA

Summary: 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 guides readers through the foundations of constrained optimization and into cutting-edge territory—including stochastic, online, and distributed settings—by uniting deep theoretical insights with practical considerations, and uses a clear narrative, rigorous proofs, and illuminating illustrations to demystify adaptive variants, away-steps, and the nuances of dealing with structured convex sets. Most of the algorithms in the book are implemented in the FrankWolfe.jl Julia package and available on a supplementary website. https://epubs.siam.org/doi/book/10.1137/1.9781611978568

List(s) this item appears in: New Arrivals - February 2026

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Item type	Current library	Collection	Call number	Copy number	Status	Barcode
Books	IIT Gandhinagar	General	519.6 BRA (Browse shelf(Opens below))	1	Available	036691

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Previous	519.544 SET Nonlinear filters: theory and applications	519.57 CUN Experimental design: from user studies to psychophysics	519.6 BOY Convex optimization	519.6 BRA Conditional gradient methods: from core principles to AI applications	519.6 NOC Numerical optimization	519.6 SAN Optimal transport for applied mathematicians: calculus of variations	519.602462 BEL Optimization concepts and applications in engineering	Next

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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 guides readers through the foundations of constrained optimization and into cutting-edge territory—including stochastic, online, and distributed settings—by uniting deep theoretical insights with practical considerations, and uses a clear narrative, rigorous proofs, and illuminating illustrations to demystify adaptive variants, away-steps, and the nuances of dealing with structured convex sets. Most of the algorithms in the book are implemented in the FrankWolfe.jl Julia package and available on a supplementary website. https://epubs.siam.org/doi/book/10.1137/1.9781611978568

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