2 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
Max Zimmer, Christophe Roux, Moritz Wagner +2
The resource requirements of neural networks can be significantly reduced through pruning - the removal of seemingly less important parameters. However, for LLMs, full retraining t…
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…
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…
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…