arXiv AI
Optimization Techniques for Solving the Minimum Red--Blue Separating Set Problem with an Application to Human--AI Text Separation
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arXiv AI
Oct 31
SAG-Sep: Sparse Augmented Graphs and Onion-Guided Search for Rounded Capacity Cut Separation
SAG‑Sep is a new separator for rounded capacity cuts (RCCs) in capacitated vehicle‑routing problems that uses a single encoder pass to score node membership across vehicle‑count levels, avoiding iterative predict‑and‑coarsen steps. It encodes the LP solution as a Sparse Augmented Graph (SAG) with flow‑informed multi‑hop edges, and leverages the nested, onion‑like structure of high‑value RCCs through a training‑free Onion‑Sweep decoder that ranks customers and emits the most violated prefixes. On the NeuralSEP benchmark with 1,000‑customer instances, SAG‑Sep with Onion‑Sweep cuts the average root gap by 17.7% over the best NeuralSEP baseline and achieves a roughly 30‑fold per‑iteration speedup.
arXiv AI
Oct 9
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation
The paper introduces a new automated feature extraction method for algorithm selection in constraint optimisation, using graph conversion and Weisfeiler‑Lehman (WL) graph kernels to produce structural representations of problem instances. It proposes a cut‑based WL feature (“WLc”) that captures structural partitions and claims this representation improves predictive performance over existing tools like “fzn2feat”. Experiments on MiniZinc Challenge instances for Borda‑count maximisation and predictive‑accuracy maximisation show that SVMs with WLc features outperform “fzn2feat”, while Random Forests and MLPs yield comparable results.
Hugging Face Trending Papers
Oct 8
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation
The paper introduces a new automated feature extraction method for algorithm selection in constraint optimisation, using graph conversion and Weisfeiler‑Lehman (WL) graph kernels to create structural representations of problem instances. It proposes a cut‑based WL feature (“WLc”) that captures structural partitions and provides a more detailed predictive signal. Experiments on MiniZinc Challenge instances show that WLc features outperform the traditional “fzn2feat” when used with Support Vector Machines, while results with Random Forests and Multi‑Layer Perceptrons are comparable.
arXiv AI
Aug 6
Approximate Multi-Objective Search Under Rulebooks
arXiv:2608. 04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance.
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
Jul 31
LM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning
arXiv:2607. 28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generalizing poorly outside the training distribution.