Asymptotic Convergence of the Frank-Wolfe Algorithm for Monotone Variational Inequalities.

This paper analyzes the asymptotic convergence of the Frank-Wolfe algorithm when applied to monotone variational inequalities. This work has direct relevance for optimization problems in various fields, including machine learning, economics, and engineering, where efficient algorithms for solving variational inequalities are highly sought after.