Algorithm Unrolling

Algorithm unrolling transforms iterative optimization algorithms into deep neural networks by "unrolling" their iterative steps into layers. This approach aims to leverage the interpretability of classical algorithms while improving their performance through learned parameters, often focusing on gradient descent variants and proximal methods within architectures like gradient descent networks (GDNs). Current research emphasizes improving the efficiency and accuracy of backpropagation through unrolled solvers, exploring connections to bilevel optimization, and applying the technique to diverse problems including inverse problems, robust principal component analysis, and online optimization with constraints. The resulting models offer a powerful blend of theoretical understanding and data-driven optimization, leading to advancements in various fields like image processing, signal reconstruction, and wireless network optimization.