Braun, Gabor <br/><br/>
Conditional gradient methods: from core principles to AI applications 

 - Philadelphia:  Society for Industrial and Applied Mathematics (SIAM),  2025. 
 - ix, 195p.:  col., ill.; pbk.:  25 cm. 
 - MOS-SIAM Series on Optimization .
<br/><br/>Include Bibliography, Glossary and Index 
<br/><br/> 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 
<br/><br/><label>ISBN: </label>9781611978551 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Conditional Gradient Methods<br/>Frank–Wolfe Algorithm<br/>Constrained Optimization<br/>First-order Methods<br/>Linear Minimization Oracle (LMO)<br/>Projection-free Algorithms<br/>Convex Optimization<br/>Adaptive Step Sizes<br/>Away-Step Frank–Wolfe<br/>Fully-Corrective Frank–Wolfe<br/>Mathematical Optimization Society
<br/><br/><label>Dewey Class. No.: </label>519.6 BRA