181 citations · 620 across the 171 of their papers we have counts for
9 papers · 1 filter
Deep Neural Network Training with Frank-Wolfe
Sebastian Pokutta, Christoph Spiegel, Max Zimmer
This paper studies the empirical efficacy and benefits of using projection-free first-order methods in the form of Conditional Gradients, a.k.a. Frank-Wolfe methods, for training N…
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…
Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices
Boro Sofranac, Ambros Gleixner, Sebastian Pokutta
Fast domain propagation of linear constraints has become a crucial component of today's best algorithms and solvers for mixed integer programming and pseudo-boolean optimization to…
Restarting Algorithms: Sometimes there is Free Lunch
Sebastian Pokutta
In this overview article we will consider the deliberate restarting of algorithms, a meta technique, in order to improve the algorithm's performance, e.g., convergence rates or app…
A Unifying Perspective on Descent Directions for Constrained Minimization and Switching Criteria
Hassan Mortagy, Swati Gupta, Sebastian Pokutta
Descent directions are fundamental to first-order methods for constrained convex optimization. In this work, we provide a unified perspective of these directions as they arise with…
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…
IPBoost -- Non-Convex Boosting via Integer Programming
Marc E. Pfetsch, Sebastian Pokutta
Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification…
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Alfredo Torrico, Mohit Singh, Sebastian Pokutta
Submodular maximization has been widely studied over the past decades, mostly because of its numerous applications in real-world problems. It is well known that the standard greedy…
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…