AT-SKM-Net: An Accelerated Trainable Sampling Kaczmarz-Motzkin Framework for Linear Hard-Constraint Feasibility on Dynamic Graphs
This work introduces a hybrid sampling strategy guided by a topology-aware heterogeneous GNN model and employs a Cholesky Update mechanism that theoretically reduces the equality projection complexity from O(N^3) to O(N^2) under low-rank perturbations.
Xiao-Chen Zhang, Hao-Yu Zhu, Yao Zhang et al.
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