Cross-Graph Attention Fusion for Learning to Branch in Chance-Constrained MILPs
Learning to branch accelerates branch-and-bound (B&B) for mixed-integer linear programming (MILP) by replacing hand-designed variable-selection rules with data-driven policies. For the large-scale MILPs produced by the sample average approximation (SAA) of chance-constrained programming (CCP), recent work encodes each...