Research map / Learning theory and algorithms / Submodular and game algorithms
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use POMC with 20,000 evaluations between changes instead of GREEDY | Dynamic max cut, n=200, k=1, density 0.01 · Changes 151--200 | mean cut value: 103.1 -> 105.2 | Helped |
| Use POMC with 20,000 evaluations between changes instead of GREEDY | Dynamic max cut, n=200, k=10, density 0.05 · Changes 1--50 | mean cut value: 282.7 -> 279.9 | Hurt |
| Use POMC with 20,000 evaluations between changes instead of GREEDY | Dynamic max cut, n=200, k=1, density 0.2 · Changes 101--150 | mean cut value: 1121 -> 1126 | Helped |
| Increase evaluations between changes from 5,000 to 20,000 | POMC on dynamic max cut · Dynamic max cut experiments | mean cut value | Helped |
| Increase the number of partitions | POMC on dynamic max cut · Dynamic max cut experiments | mean cut value relative to GREEDY | Hurt |
| Maintain the non-dominated population across threshold changes rather than restart | POMC on dynamic partition matroid constraints · Dynamic max cut | mean cut value | Helped |
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