A GNN is trained to predict adaptive step sizes and weights for distributed ADMM by unrolling a fixed number of iterations and minimizing solution error on a problem class.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing.IEEE Signal Processing Magazine, 38(2):18–44,
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Learning to accelerate distributed ADMM using graph neural networks
A GNN is trained to predict adaptive step sizes and weights for distributed ADMM by unrolling a fixed number of iterations and minimizing solution error on a problem class.