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-based branch-and-bound approach -- to address nonconvex quadratic objective and constraints. We reformulate nonlinear constraints, introduce preprocessing steps, and a suite of heuristics including rounding strategies, gradient-guided selection, and large neighborhood search techniques that exploit integer-feasible vertices generated during the Frank-Wolfe iterations. Computational results demonstrate the effectiveness of our method in solving challenging MIQCQPs, achieving improvements on QPLIB instances within minutes and winning first place in the Land-Doig MIP Computational Competition 2025.
@article{arxiv.2508.01299,
title = {A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization},
author = {Gioni Mexi and Deborah Hendrych and Sébastien Designolle and Mathieu Besançon and Sebastian Pokutta},
journal= {arXiv preprint arXiv:2508.01299},
year = {2025}
}