10 citations · 26 across the 6 of their papers we have counts for
7 papers
Conditional Gradient Methods
Gábor Braun, Alejandro Carderera, Cyrille W. Combettes +4
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithm…
The Iterates of the Frank-Wolfe Algorithm May Not Converge
Jérôme Bolte, Cyrille W. Combettes, Édouard Pauwels
The Frank-Wolfe algorithm is a popular method for minimizing a smooth convex function over a compact convex set . While many convergence results have been derived…
Complexity of Linear Minimization and Projection on Some Sets
Cyrille W. Combettes, Sebastian Pokutta
The Frank-Wolfe algorithm is a method for constrained optimization that relies on linear minimizations, as opposed to projections. Therefore, a motivation put forward in a large bo…
Projection-Free Adaptive Gradients for Large-Scale Optimization
Cyrille W. Combettes, Christoph Spiegel, Sebastian Pokutta
The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set. In this respect, stochastic Frank-Wolfe algorithms occup…
Boosting Frank-Wolfe by Chasing Gradients
Cyrille W. Combettes, Sebastian Pokutta
The Frank-Wolfe algorithm has become a popular first-order optimization algorithm for it is simple and projection-free, and it has been successfully applied to a variety of real-wo…
Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm
Cyrille W. Combettes, Sebastian Pokutta
The approximate Carathéodory theorem states that given a compact convex set and , each point can be…
Blended Matching Pursuit
Cyrille W. Combettes, Sebastian Pokutta
Matching pursuit algorithms are an important class of algorithms in signal processing and machine learning. We present a blended matching pursuit algorithm, combining coordinate de…