AI RESEARCH
Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
arXiv CS.LG
•
ArXi:2605.09382v1 Announce Type: new The Linear Assignment Problem (LAP) is a fundamental combinatorial optimization task with applications ranging from computer vision to logistics. Classical exact solvers such as the Hungarian and Jonker-Volgenant (LAPJV) algorithms guarantee optimality, but their cubic time complexity $\mathcal{O}(N^{3})$ becomes a bottleneck for large-scale instances. Recent learning-based approaches aim to replace these solvers with neural models, often sacrificing exactness or failing to scale due to memory constraints.