A graph neural network architecture that is trained to predict the truth value of each MaxSAT variable in an optimal solution (with a confidence score) is proposed, and significant improvements in performance are demonstrated compared to a current branch-and-bound MaxSAT solver.
The Weighted Partial MaxSAT (WPMS) problem requires finding an assignment that satisfies all hard clauses while maximizing the total weight of satisfied soft clauses. From a computational perspective, WPMS is not merely an extension of the uniform-weight Partial MaxSAT (PMS); rather, it requires search strategies that...
Jialu Zhang, Chumin Li, Sami Cherif et al.· Proceedings of the Thirty-Fi...· 0 citations
A preliminary comparison with the A ∗ -based algorithms indicates that they are outperformed by the ILP-based approaches on graphs exceeding ten nodes, and a dataset of optimal edit paths across widely used datasets from the TUDataset is released.
Andrea D'Ascenzo, Julian Meffert, Petra Mutzel et al.· 0 citations
We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules. We propose mixed integer linear programming (MILP) formulations for training sparse disjunctive normal form (DNF) networks, viewed as small networks with a conjunctive hidden layer and a...
Thomas Pammer, Johannes Fürnkranz, Markus Sinnl· 0 citations
This work proposes graph-aware hybrid approach that combines classical graph algorithms and Large Language Models (LLMs) to improve interpretability of the solutions for combinatorial optimization problems. The proposed framework is capable of solving such NP-hard problems as Traveling Salesman Problem (TSP), Graph Col...
Dasu Vaman Ravi Prasad, Aditya Kulkarni, Srihaasa Devayajanam et al.· 2026 International Conferenc...· 0 citations
Algorithm Selection is essential for efficient Constraint Programming. Over the years, many algorithm selectors based on machine learning methods have been successfully applied, yet traditional feature extraction methods often rely on manually decided instance-level statistics that fail to capture the underlying proble...
This paper finds that solutions produced at the early search stage of MILP solvers are often structurally close to the solutions found after full-budget search, and proposes a new solver-informed paradigm that shifts the learning target from variable assignment to early-to-final consistency.
Guanli Li, Chengrui Gao, Chenguang Wang et al.· 0 citations
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