Unrolled Optimization

Unrolled optimization integrates iterative optimization algorithms directly into the architecture of neural networks, aiming to improve the efficiency and interpretability of deep learning models for various tasks. Current research focuses on addressing challenges like memory efficiency and backward pass convergence, often employing techniques such as stochastic mini-batching, folded optimization, and persistent evolution strategies within unrolled architectures like convolutional neural networks and graph neural networks. This approach shows promise in diverse applications, including image reconstruction, federated learning, and solving inverse problems, by offering a more efficient and interpretable alternative to traditional black-box deep learning methods.