3 papers
cs.LG2026
Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices
Qingyu Han, Qian Li, Linxin Yang +3
Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as a promising paradigm, with Grap…
math.OC2025
On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks
Chenyang Wu, Qian Chen, Akang Wang +4
Convex quadratically constrained quadratic programs (QCQPs) involve finding a solution within a convex feasible region defined by quadratic constraints while minimizing a convex qu…
math.OC2025
SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization
Qian Chen, Tianjian Zhang, Linxin Yang +5
Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized t…