Conditional Gradient Methods

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