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Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning (NeurIPS 2021)

Code for reproducing the experimental results of our paper: https://arxiv.org/pdf/2106.01854.pdf

Requirements

  • Python = 3.8.10
  • PyTorch = 1.9.0
  • Pytorch_Geometric = 1.7.2
  • pyamg = 4.1.0
  • networkx = 2.6.2
  • pygmsh = 7.1.6
  • gmsh = 4.8.0

Training

python main.py

Files

  • Batch_Graph.py: The class for making a batch of grids to be the input to the agent's network.
  • DuelingNet.py: The netwrok architecture that the agent utilizes (TAGCN network).
  • Lloyd_Unstructured.py: The code for the generating Lloyd agregations on grids.
  • MG_Agent.py: The class for Dueling Double DQN agent.
  • Optim.py: The code for running the grid coarsinging using Lloyed aggregation.
  • Solve_MG.py: The code for the two grid cycle AMG algorithm.
  • Unstructured.py: The class for defining grids and making unstructured meshes.
  • fem.py: Constructs a finite element discretization of a 2D Poisson problem.
  • main.py: The main driver for training the RL agent and test functions.