4 papers
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.…
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…