181 citations · 620 across the 171 of their papers we have counts for
11 papers · 2 filters
Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods
Deborah Hendrych, Mathieu Besançon, Sebastian Pokutta
We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. T…
Strong Convexity of Sets in Riemannian Manifolds
Damien Scieur, David Martínez-Rubio, Thomas Kerdreux +2
Curvature properties of convex objects, such as strong convexity, are important in designing and analyzing convex optimization algorithms in the Hilbertian or Riemannian settings.…
The Frank-Wolfe algorithm: a short introduction
Sebastian Pokutta
In this paper we provide an introduction to the Frank-Wolfe algorithm, a method for smooth convex optimization in the presence of (relatively) complicated constraints. We will pres…
Splitting the Conditional Gradient Algorithm
Zev Woodstock, Sebastian Pokutta
We propose a novel generalization of the conditional gradient (CG / Frank-Wolfe) algorithm for minimizing a smooth function under an intersection of compact convex sets, using…
Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes
Elias Wirth, Javier Pena, Sebastian Pokutta
Recent papers have shown that the Frank-Wolfe algorithm (FW) with open-loop step-sizes exhibits rates of convergence faster than the iconic rate. In particula…
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
David Martínez-Rubio, Christophe Roux, Christopher Criscitiello +1
In this work, we study optimization problems of the form , where is defined on a product Riemannian manifold and i…
Learning Cuts via Enumeration Oracles
Daniel Thuerck, Boro Sofranac, Marc E. Pfetsch +1
Cutting-planes are one of the most important building blocks for solving large-scale integer programming (IP) problems to (near) optimality. The majority of cutting plane approache…
Data-driven Distributionally Robust Optimization over Time
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun +5
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters…
Online Learning for Scheduling MIP Heuristics
Antonia Chmiela, Ambros Gleixner, Pawel Lichocki +1
Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Sin…
Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
David Martínez-Rubio, Elias Wirth, Sebastian Pokutta
It has recently been shown that ISTA, an unaccelerated optimization method, presents sparse updates for the -regularized personalized PageRank problem, leading to cheap ite…
On a Frank-Wolfe Approach for Abs-smooth Functions
Timo Kreimeier, Sebastian Pokutta, Andrea Walther +1
We propose an algorithm which appears to be the first bridge between the fields of conditional gradient methods and abs-smooth optimization. Our problem setting is motivated by var…