About this ebook
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. Implementations of most of the algorithms in the book are available on a supplementary website or in the FrankWolfe.jl Julia package.
This book is intended for optimization researchers and theorists, machine learning methodologists, and algorithm designers. Graduate students in those areas will also find it of interest.
Discover more
About the author
Gábor Braun is a member of the Zuse Institute Berlin.
Alejandro Carderera is a senior applied researcher at GitHub, working in Copilot's Applied Science and Models team, focusing on code completions and code review.
Cyrille W. Combettes is a quantitative researcher at Capital Fund Management in Paris.
Hamed Hassani is an associate professor in the Department of Electrical and Systems Engineering, the Department of Computer and Information Systems, and the Department of Statistics and Data Science at the University of Pennsylvania.
Amin Karbasi is chief scientist at Robust Intelligence and a professor at Yale University.
Aryan Mokhtari is an assistant professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin, where he holds the Fellow of Texas Instruments/Kilby.
Sebastian Pokutta is vice president of the Zuse Institute Berlin (ZIB) and a professor at TU Berlin.
