arXiv Machine Learning
Jun 18
FORGE: Foundational Optimization Representations from Graph Embeddings
arXiv:2508. 20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering.
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
arXiv:2604. 23053v2 Announce Type: replace Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization.
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arXiv Machine Learning
Jul 27
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.
arXiv AI
Jun 9
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
arXiv Machine Learning
Jun 2
Regularized Large Neighborhood Search
arXiv:2606. 02294v1 Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables.
arXiv Machine Learning
Jun 2
Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
arXiv:2606. 01081v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy.
arXiv AI
Jun 2
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.
arXiv Machine Learning
Aug 5
BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
arXiv:2608. 02612v1 Announce Type: cross Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise.
arXiv Machine Learning
Aug 4
Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection
arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.
arXiv Machine Learning
Aug 11
Input convex neural networks as surrogates in mathematical optimisation
arXiv:2608. 09707v1 Announce Type: cross Abstract: Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research.
arXiv AI
Jun 2
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
arXiv:2307. 05213v3 Announce Type: replace-cross Abstract: Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.
arXiv Machine Learning
Jul 16
Learned Pairwise Deep Dual-Optimal Inequalities for Stabilizing Column Generation
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.
arXiv Machine Learning
Jun 25
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
arXiv:2606. 25832v1 Announce Type: new Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs).