10 citations · 24 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…
Scalable Frank-Wolfe on Generalized Self-concordant Functions via Simple Steps
Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta
Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe vari…
FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients
Mathieu Besançon, Alejandro Carderera, Sebastian Pokutta
We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is d…
Parameter-free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin +1
Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but li…
CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery
Alejandro Carderera, Sebastian Pokutta, Christof Schütte +1
Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies.…
Second-order Conditional Gradient Sliding
Alejandro Carderera, Sebastian Pokutta
Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, due to their local quadratic convergence. The…
Locally Accelerated Conditional Gradients
Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta
Conditional gradients constitute a class of projection-free first-order algorithms for smooth convex optimization. As such, they are frequently used in solving smooth convex optimi…