Scalable Frank-Wolfe on Generalized Self-concordant Functions via Simple Steps

1 code implementation • NeurIPS 2021 • Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta

Generalized self-concordance is a key property present in the objective function of many important learning problems.

Parameter-free Locally Accelerated Conditional Gradients

no code implementations • 12 Feb 2021 • Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta

Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but linear optimization over the constraint set remains computationally feasible.

CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery

1 code implementation • 7 Jan 2021 • Alejandro Carderera, Sebastian Pokutta, Christof Schütte, Martin Weiser

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.

Dynamical Systems Applications

Second-order Conditional Gradient Sliding

1 code implementation • 20 Feb 2020 • 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.

Locally Accelerated Conditional Gradients

no code implementations • 19 Jun 2019 • Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta

As such, they are frequently used in solving smooth convex optimization problems over polytopes, for which the computational cost of orthogonal projections would be prohibitive.