Research map / Learning theory and algorithms / Combinatorial and search optimization
Assortment Optimization Under the Mallows model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| strengthen MIP upper-bound constraints using structural properties of the optimal solution (ablation) | MIP for Mallows assortment · n=25 | running time | Helped |
| increase n from 10 to 25 | strengthened MIP · e^-θ = 0.9, average running time | running time (s): 4.72 -> 769.78 | Hurt |
| increase n from 10 to 25 | strengthened MIP · e^-θ = 0.8, average running time | running time (s): 4.60 -> 143.21 | Hurt |
| increase e^-θ from 0.8 to 0.9 | strengthened MIP · n=25 average running time | running time (s): 143.21 -> 769.78 | Hurt |
| increase e^-θ from 0.8 to 0.9 | strengthened MIP · n=25 max running time | running time (s): 183.78 -> 1,817.98 | Hurt |
| increase e^-θ from 0.8 to 0.9 | strengthened MIP · n=10 average running time | running time (s): 4.60 -> 4.72 | No clear effect |
| increase e^-θ from 0.8 to 0.9 | strengthened MIP · n=20 average running time | running time (s): 48.08 -> 105.30 | Hurt |
| extend single Mallows to finite mixture of Mallows models | MIP formulation · choice probability | applicability | Helped |
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