181 citations · 620 across the 171 of their papers we have counts for
18 papers · 2 filters
Graph Isomorphism: Mixed-Integer Convex Optimization from First-Order Methods
Wenjie Xiao, Mathieu Besançon, Patrick Gelß +3
The graph isomorphism (GI) problem, which asks whether two graphs are structurally identical, occupies a unique position in computational complexity -- it is neither known to be so…
Objective Coefficient Rounding and Almost Symmetries in Binary Programs
Dominik Kuzinowicz, Paweł Lichocki, Gioni Mexi +3
This article investigates the interplay of rounding objective coefficients in binary programs and almost symmetries. Empirically, reducing the number of significant bits through ro…
The SCIP Optimization Suite 10.0
Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva +31
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This…
Convex semidefinite tensor optimization and quantum entanglement
Liding Xu, Ye-Chao Liu, Sebastian Pokutta
The cone of positive-semidefinite (PSD) matrices is fundamental in convex optimization, and we extend this notion to tensors, defining PSD tensors, which correspond to separable qu…
Boscia.jl: A review and tutorial
Wenjie Xiao, Deborah Hendrych, Mathieu Besançon +1
Mixed-integer nonlinear optimization (MINLP) comprises a large class of problems that are challenging to solve and exhibit a wide range of structures. The Boscia framework (Hendryc…
Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
Sebastian Pokutta
We consider the problem of minimizing a difference of (smooth) convex functions over a compact convex feasible region , i.e., , with smooth and L…
A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
Gioni Mexi, Deborah Hendrych, Sébastien Designolle +2
We propose a primal heuristic for quadratic mixed-integer problems. Our method extends the Boscia framework -- originally a mixed-integer convex solver leveraging a Frank-Wolfe-bas…
Efficient Quadratic Corrections for Frank-Wolfe Algorithms
Jannis Halbey, Seta Rakotomandimby, Mathieu Besançon +2
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended conditional gradients, blended pairwise conditional gradients, and full…
Adaptive Open-Loop Step-Sizes for Accelerated Convergence Rates of the Frank-Wolfe Algorithm
Elias Wirth, Javier Peña, Sebastian Pokutta
Recent work has shown that in certain settings, the Frank-Wolfe algorithm (FW) with open-loop step-sizes for a fixed parameter $\ell \in \mathbb{N},\, \…
Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes
Gabriele Iommazzo, David Martínez-Rubio, Francisco Criado +2
We study the linear convergence of Frank-Wolfe algorithms over product polytopes. We analyze two condition numbers for the product polytope, namely the \emph{pyramidal width} and t…
Fast Frank--Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions
Shota Takahashi, Sebastian Pokutta, Akiko Takeda
We propose Frank--Wolfe (FW) algorithms with an adaptive Bregman step-size strategy for smooth adaptable (also called: relatively smooth) (weakly-) convex functions. This means tha…
Scenario Reduction for Distributionally Robust Optimization
Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers +2
Stochastic and (distributionally) robust optimization problems often become computationally challenging as the number of scenarios or data points increases. Scenario reduction is t…
Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks
Hannah Troppens, Mathieu Besançon, St. Elmo Wilken +1
Constraint-based metabolic models can be used to investigate the intracellular physiology of microorganisms. These models couple genes to reactions, and typically seek to predict m…
Beyond Short Steps in Frank-Wolfe Algorithms
David Martínez-Rubio, Sebastian Pokutta
We introduce novel techniques to enhance Frank-Wolfe algorithms by leveraging function smoothness beyond traditional short steps. Our study focuses on Frank-Wolfe algorithms with s…
Implicit Riemannian Optimism with Applications to Min-Max Problems
Christophe Roux, David Martínez-Rubio, Sebastian Pokutta
We introduce a Riemannian optimistic online learning algorithm for Hadamard manifolds based on inexact implicit updates. Unlike prior work, our method can handle in-manifold constr…
State-of-the-art Methods for Pseudo-Boolean Solving with SCIP
Gioni Mexi, Dominik Kamp, Yuji Shinano +8
The Pseudo-Boolean problem deals with linear or polynomial constraints with integer coefficients over Boolean variables. The objective lies in optimizing a linear objective functio…
Secant Line Search for Frank-Wolfe Algorithms
Deborah Hendrych, Mathieu Besançon, David Martínez-Rubio +1
We present a new step-size strategy based on the secant method for Frank-Wolfe algorithms. This strategy, which requires mild assumptions about the function under consideration, ca…
Improved algorithms and novel applications of the FrankWolfe.jl library
Mathieu Besançon, Sébastien Designolle, Jannis Halbey +6
Frank-Wolfe (FW) algorithms have emerged as an essential class of methods for constrained optimization, especially on large-scale problems. In this paper, we summarize the algorith…