Abstract:The integration of constrained optimization models as components in deep networks has led to promising advances in both these domains. A primary challenge in this setting is backpropagation through the optimization mapping, which typically lacks a closed form. A common approach is unrolling, which relies on automatic differentiation through the operations of an iterative solver. While flexible and general, unrolling can encounter accuracy and efficiency issues in practice. These issues can be avoided by differ… Show more
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