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洞見 - End-to-end training in ML models
CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs
CaVE proposes a novel approach for end-to-end training in binary linear programs, aligning cost vectors to optimal subcones efficiently.
CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs
CaVE proposes a novel approach for efficient end-to-end training of ML models to predict cost coefficients of binary linear optimization problems, achieving a favorable trade-off between training time and solution quality.
