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Tests codecov docs PyPI PyPI - Downloads GPLv3

deep-unfolding: Deep unfolding of iterative methods

The deep-unfolding package includes iterative methods for solving linear equations. However, due to the various parameters and performance characteristics of the iterative approach, it is necessary to optimize these parameters to improve the convergence rate. deep-unfolding takes an iterative algorithm with a fixed number of iterations $T$, unravels its structure, and adds trainable parameters. These parameters are then trained using deep learning techniques such as loss functions, stochastic gradient descent, and backpropagation.

The package contains two different modules containing iterative methods. The first, methods, includes conventional iterative methods. The second, train_methods, includes deep unfolding versions of the conventional methods.

Installation

pip install --upgrade pip
pip install deep-unfolding

Quick start

from deep_unfolding import device, evaluate_model, generate_A_H_sol, SORNet, train_model
from torch import nn, optim

total_itr = 25  # Total number of iterations
n = 300  # Number of rows
m = 600  # Number of columns
bs = 10000  # Mini-batch size (samples)
num_batch = 500  # Number of mini-batches
lr_adam = 0.002  # Learning rate of optimizer
init_val_SORNet = 1.1  # Initial value of omega for SORNet

seed = 12

A, H, W, solution, y = generate_A_H_sol(n=n, m=m, seed=seed, bs=bs)
loss_func = nn.MSELoss()

# Model
model_SorNet = SORNet(A, H, bs, y, init_val_SORNet, device=device)

# Optimizer
opt_SORNet = optim.Adam(model_SorNet.parameters(), lr=lr_adam)

trained_model_SorNet, loss_gen_SORNet = train_model(model_SorNet, opt_SORNet, loss_func, solution, total_itr, num_batch)

norm_list_SORNet = evaluate_model(trained_model_SorNet, solution, n, bs, total_itr, device=device)

Package contents

This package implements various iterative techniques for approximating the solutions of linear problems of the type $Ax = b$. The conventional methods implemented in the methods module are:

  • GS: Gauss-Seidel (GS) algorithm
  • RI: Richardson iteration algorithm
  • Jacobi: Jacobi iteration (RI) algorithm
  • SOR: Successive Over-Relaxation (SOR) algorithm
  • SORCheby: Successive Over-Relaxation (SOR) with Chebyshev acceleration algorithm
  • AOR: Accelerated Over-Relaxation (AOR) algorithm
  • AORCheby: Accelerated Over-Relaxation (AOR) with Chebyshev acceleration algorithm

This package also implements several models based on Deep Unfolding Learning, enabling optimization of the parameters of some of the preceding algorithms to obtain an optimal approximation. The models implemented in the module train_methods are:

  • SORNet: Optimization via Deep Unfolding Learning of the Successive Over-Relaxation (SOR) algorithm
  • SORChebyNet: Optimization via Deep Unfolding Learning of the Successive Over-Relaxation (SOR) with Chebyshev acceleration algorithm
  • AORNet: Optimization via Deep Unfolding Learning of the Accelerated Over-Relaxation (AOR) algorithm
  • RINet: Optimization via Deep Unfolding Learning of the Richardson iteration (RI) algorithm

Reference

If you use this software, please cite the following reference: available soon

License

GPLv3 License