2026· International Conference on Principles and Practice of Constraint Programming· pp. 61:1-61:18· 0 citations· 34 references
Computer Science
TL;DR
A reusable self-supervised framework for edge-selection optimization that learns directly from unlabeled instances is proposed, and a lightweight graph architecture centered on a cost-attention convolution is introduced, where edge costs and feasibility information directly shape message passing.
A multi-scale deep optimization model based on an encoder-decoder architecture that validates the effectiveness of the multi-scale EMA and Triplet-Reasoning mechanisms, providing a new direction for deep learning-based graph optimization research.
Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information latent in a co-sampled solution group. Preference-optimization methods anchor on the single best solution and discard fine-grained quality and structural signal from...
Yuan-Yu Li, Jin-Tao Xu, Zijiang Liu et al.· 0 citations
The Dual-GNN Multilevel Coarsening framework uses learning to guide multilevel graph coarsening while retaining combinatorial search for final decision making and achieves the best mean solution quality among all evaluated methods.
This study introduces E2E_GERL, a novel end-to-end graph-embedded reinforcement learning algorithm for the time-constrained SPP, which achieves better results with substantially lower inference time than classical and NCO baselines, which also validate the potential of integrating NCO into constrained optimization prob...
Shu-Hao Yang, Min Huang, Sheng-Xiang Yang et al.· Mathematics· 0 citations
TAST is proposed, a Task-Aware Sparse Topology Learning framework that constructs task-specific subnetworks within a shared network that learns shared parameters and task-specific topologies, hardens the learned masks into binary structures, and optionally applies a lightweight sample-conditioned refinement module for...
Constrained Graph Diffusion, a graph-based generative diffusion model that learns the discrete component of mixed-integer optimization problems while integrating a training-free feasibility projection operator directly into the reverse diffusion process to steer intermediate samples toward the feasible set throughout g...
Vincenzo Di Vito, Mehdi Taghizadeh, Deepjyoti Deka et al.· 0 citations
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