Deep Unfolding: Recent Developments, Theory, and Design Guidelines
This is an incremental review article that synthesizes existing developments in deep unfolding for researchers in signal processing and ML, offering design guidelines and theoretical insights without presenting new methods or data.
The paper tackles the challenge of bridging classical iterative optimization algorithms with machine learning by introducing deep unfolding as a framework to transform optimization solvers into trainable ML architectures, providing a tutorial overview with theoretical and empirical analysis of trade-offs in complexity, interpretability, and robustness.
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization algorithms provide interpretability and theoretical guarantees, they often rely on surrogate objectives, require careful hyperparameter tuning, and exhibit substantial computational latency. Conversely, machine learning (ML ) offers powerful data-driven modeling capabilities but lacks the structure, transparency, and efficiency needed for optimization-driven inference. Deep unfolding has recently emerged as a compelling framework that bridges these two paradigms by systematically transforming iterative optimization algorithms into structured, trainable ML architectures. This article provides a tutorial-style overview of deep unfolding, presenting a unified perspective of methodologies for converting optimization solvers into ML models and highlighting their conceptual, theoretical, and practical implications. We review the foundations of optimization for inference and for learning, introduce four representative design paradigms for deep unfolding, and discuss the distinctive training schemes that arise from their iterative nature. Furthermore, we survey recent theoretical advances that establish convergence and generalization guarantees for unfolded optimizers, and provide comparative qualitative and empirical studies illustrating their relative trade-offs in complexity, interpretability, and robustness.