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MIPaaL: Mixed Integer Program as a Layer

AAAI 2020 · Learning theory and algorithms › Combinatorial and search optimization · approach: Learning branch-and-cut for MILP · 98 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train with the exact MIP layer rather than the LP relaxationMIPaaL · SP500monthly percentage increase: 1.97 $\pm$ 0.17 -> 2.79 $\pm$ 0.17Helped
Train with the exact MIP layer rather than separate prediction and optimizationMIPaaL · DAXmonthly percentage increase: 0.70 $\pm$ 1.46 -> 5.70 $\pm$ 0.68Helped
Train with the exact MIP layer rather than separate prediction and optimizationMIPaaL · Diverse Matchingsuccessful matches: 3.42 $\pm$ 0.78 -> 4.80 $\pm$ 0.71Helped
Limit cut generation to 1000 cuts instead of generating cuts exactly (ablation)MIPaaL · SP500monthly percentage increase: 2.79 $\pm$ 0.17 -> 2.60 $\pm$ 0.16Hurt
Limit cut generation to 1000 cuts instead of generating cuts exactly (ablation)MIPaaL · DAXmonthly percentage increase: 5.70 $\pm$ 0.68 -> 4.39 $\pm$ 0.66Hurt
Limit cut generation to 1000 cuts instead of generating cuts exactly (ablation)MIPaaL · Diverse Matchingsuccessful matches: 4.80 $\pm$ 0.71 -> 3.45 $\pm$ 0.71Hurt
Limit cut generation to 100 cuts instead of generating cuts exactly (ablation)MIPaaL · SP500monthly percentage increase: 2.79 $\pm$ 0.17 -> 1.25 $\pm$ 0.14Hurt
Limit cut generation to 100 cuts instead of generating cuts exactly (ablation)MIPaaL · DAXmonthly percentage increase: 5.70 $\pm$ 0.68 -> 0.35 $\pm$ 0.63Hurt
Limit cut generation to 100 cuts instead of generating cuts exactly (ablation)MIPaaL · Diverse Matchingsuccessful matches: 4.80 $\pm$ 0.71 -> 2.57 $\pm$ 0.54Hurt

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