TY  - GEN
AU  - Braun, Gabor
AU  -  Carderera, Alejandro 
AU  - Combettes, Cyrille W. 
AU  - Hassani, Hamed 
AU  - Karbasi, Amin 
AU  - Mokhtari, Aryan 
AU  - Pokutta, Sebastian 
TI  - Conditional gradient methods: from core principles to AI applications
SN  - 9781611978551
U1  - 519.6 BRA 
PY  - 2025///
CY  - Philadelphia
PB  - Society for Industrial and Applied Mathematics (SIAM)
KW  - Conditional Gradient Methods
KW  - Frank–Wolfe Algorithm
KW  - Constrained Optimization
KW  - First-order Methods
KW  - Linear Minimization Oracle (LMO)
KW  - Projection-free Algorithms
KW  - Convex Optimization
KW  - Adaptive Step Sizes
KW  - Away-Step Frank–Wolfe
KW  - Fully-Corrective Frank–Wolfe
KW  - Mathematical Optimization Society
N1  - Include Bibliography, Glossary and Index
N2  - 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
ER  -