BPQP reformulates the backward pass of differentiable convex optimization layers as an equality-constrained quadratic program, allowing fast ADMM-based solvers to compute gradients.
Melding the data-decisions pipeline: Decision- focused learning for combinatorial optimization
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BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
BPQP reformulates the backward pass of differentiable convex optimization layers as an equality-constrained quadratic program, allowing fast ADMM-based solvers to compute gradients.