DiffAPQP accelerates end-to-end decision-focused learning on IEEE 118-bus power systems by 2.27x to 6.39x over CvxpyLayers while keeping decision costs within 0.2%.
Towards improving unit commitment economics: An add-on tailor for renewable energy and reserve predic- tions,
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Structured Differentiable Optimization for Efficient Decision-focused Learning in Power Systems
DiffAPQP accelerates end-to-end decision-focused learning on IEEE 118-bus power systems by 2.27x to 6.39x over CvxpyLayers while keeping decision costs within 0.2%.