Research map / Learning theory and algorithms / Combinatorial and search optimization
Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming (Student Abstract)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace LNS with ACP using SCIP as the subroutine solver | Large-scale IPs · IS, MVC, MAXCUT, SC, and real-world IP | objective value: IS: 194733.92 $\pm$ 79.17 -> 207901.54 $\pm$ 1531.18; MVC: 304860.39 $\pm$ 313.2 -> 291226.0 $\pm$ 1189.94; MAXCUT: 5538 | Helped |
| Replace LNS with ACP using Gurobi as the subroutine solver | Large-scale IPs · IS, MVC, MAXCUT, SC, and real-world IP | objective value: IS: 220368.34 $\pm$ 557.89 -> 227559.62 $\pm$ 57.26; MVC: 277870.09 $\pm$ 300.8 -> 271163.92 $\pm$ 337.0; MAXCUT: 910662 | Helped |
| Use solver-generated rather than artificially constructed initial feasible solutions (ACP2) (ablation) | ACP · Large-scale IPs using SCIP | objective value: IS: 207901.54 $\pm$ 1531.18 -> 196742.1 $\pm$ 116.6; MVC: 291226.0 $\pm$ 1189.94 -> 301836.42 $\pm$ 70.59; MAXCUT: 82959 | Mixed |
| Use solver-generated rather than artificially constructed initial feasible solutions (ACP2) (ablation) | ACP · Large-scale IPs using Gurobi | objective value: IS: 227559.62 $\pm$ 57.26 -> 227636.51 $\pm$ 432.18; MVC: 271163.92 $\pm$ 337.0 -> 271087.22 $\pm$ 295.85; MAXCUT: 10504 | Mixed |
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