Research map / Learning theory and algorithms / Combinatorial and search optimization
Learning to Branch
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Tune the linear scoring parameter to the instance distribution rather than use a fixed value | B&B variable selection · CATS arbitrary combinatorial auctions | Average B&B tree size: three times the size | Helped |
| Tune μ below μ* to branch first on the small infeasible subproblem | B&B with the linear combination of min and max scoring rules · MILP family F | B&B tree size: 2^{(n-4)/2} nodes -> O(1) nodes | Helped |
| Tune μ above μ* to select the small subproblem first | B&B with the linear combination of min and max scoring rules · MILP family G | B&B tree size: Ω(2^{(n-5)/4}) nodes -> O(1) nodes | Helped |
| Learn μ from samples instead of choosing a parameter from a fixed, data-independent discretization | B&B variable selection · MILP instances from a two-instance distribution | Expected B&B tree size: Ω(2^{(n-9)/4}) -> O(1) | Helped |
| Use the parameter optimal for regions auctions, facility location, or clustering on arbitrary auctions | B&B variable selection · CATS arbitrary combinatorial auctions | Average B&B tree size: three times the size | Hurt |
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