← Back to all news
arXiv Machine Learning By Zhengzhong Ricky You, Bo Tang, Haoran Liu, Baichuan Mo

Learned Pairwise Deep Dual-Optimal Inequalities for Stabilizing Column Generation

Read the original on arXiv Machine Learning →

arXiv:2607. 13373v1 Announce Type: cross Abstract: Column generation (CG) is central to many large-scale optimization algorithms, including branch-price-and-cut methods for vehicle routing problems, but unstable dual solutions can substantially slow its convergence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Aug 28

CG4AI: A Column Generation Framework for Training AI Models Under Constraints

CG4AI is a column generation framework that trains AI models while enforcing linear constraints on their outputs. It constructs a convex combination of models, using a master linear program to set mixture weights and a pricing subproblem to generate new models guided by dual variables, focusing on the most violated constraints. The method is applied to MNIST digit classification—demonstrating constraint learning, adversarial robustness, error correction, and output relabeling—and to multi‑commodity flow routing, achieving feasible predictors with higher accuracy than single‑model baselines.

By Youcef Magnouche, Abderrahmane Driouch, S\'ebastien Martin, Pierre Bauguion
agentsbenchmarkssafety
More like this →
Hugging Face Trending Papers
Aug 20

Learning Early-to-Final Solution Consistency for MILP Acceleration

Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making. Owing to their NP-hardness, however, modern solvers may struggle to find high-quality solutions for challenging MILP instances within practical time limits.

benchmarks
More like this →