CaVE proposes a novel approach for end-to-end training in binary linear programs, aligning cost vectors to optimal subcones efficiently.
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.