Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives
It addresses the problem of incorporating strict constraints in deep learning for researchers and practitioners, but is incremental as it synthesizes existing developments.
This paper surveys the integration of differentiable convex optimization layers into neural networks to handle hard constraints, reviewing their evolution from quadratic programming to general convex problems and analyzing theoretical foundations and applications.
The integration of optimization problems within neural network architectures represents a fundamental shift from traditional approaches to handling constraints in deep learning. While it is long known that neural networks can incorporate soft constraints with techniques such as regularization, strict adherence to hard constraints is generally more difficult. A recent advance in this field, however, has addressed this problem by enabling the direct embedding of optimization layers as differentiable components within deep networks. This paper surveys the evolution and current state of this approach, from early implementations limited to quadratic programming, to more recent frameworks supporting general convex optimization problems. We provide a comprehensive review of the background, theoretical foundations, and emerging applications of this technology. Our analysis includes detailed mathematical proofs and an examination of various use cases that demonstrate the potential of this hybrid approach. This work synthesizes developments at the intersection of optimization theory and deep learning, offering insights into both current capabilities and future research directions in this rapidly evolving field.