Conditional Gradient Methods

Nyhet

From Core Principles to AI Applications

Häftad, Engelska, 2025

Av Gábor Braun, Alejandro Carderera, Cyrille W. Combettes, Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Sebastian Pokutta

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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 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.

Produktinformation

Utgivningsdatum2025-12-15
FormatHäftad
SpråkEngelska
Antal sidor198
FörlagSociety for Industrial & Applied Mathematics,U.S.
ISBN9781611978551

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Sebastian Pokutta is Vice President of the Zuse Institute Berlin (ZIB) and Professor of Mathematics at TU Berlin. Gábor Braun is currently a member of Zuse Institute Berlin. Hamed Hassani is an associate professor of the Electrical and Systems Engineering Department, Computer and Information Systems Department, and the Department of Statistics and Data Science at the University of Pennsylvania.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. Aryan Mokhtari is an Assistant Professor in the Electrical and Computer Engineering Department of the University of Texas at Austin, where he holds the Fellow of Texas Instruments/Kilby.Cyrille Combettes is a quantitative researcher at Capital Fund Management in Paris. Amin Karbasi is Chief Scientist at Robust Intelligence and a professor at Yale University.

Conditional gradient algorithms have become an essential part of the algorithmic toolbox in machine learning, signal processing, and related fields. This monograph offers a comprehensive review of both classical results and recent generalizations, including extensions to large-scale settings. The presentation is notably clear, featuring illustrations, detailed proofs, and application examples. It will serve as an important reference for graduate students and researchers in data science."" — Francis Bach, Princeton University ""Conditional Gradient Methods is a thorough and accessible guide to one of the most versatile families of optimization algorithms. The book traces the rich history of the conditional gradient algorithm and explores its modern advancements, offering a valuable resource for both experts and newcomers. With clear explanations of the algorithms, their analysis, and practical applications, the authors provide a go-to reference for anyone tackling constrained optimization problems. This book is sure to inspire fresh ideas and drive advancements in the field."" — Elad Hazan, INRIA