Research map / Learning theory and algorithms / Combinatorial and search optimization
Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
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Problem
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Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use VCM50 instead of BGF | QUBO solver · B2500(10) | GAP (%): 0.807 -> 0.362 | Helped |
| Use VCM50 instead of BGF | QUBO solver · P3000(5) | GAP (%): 0.979 -> 0.861 | Helped |
| Use VCM50 instead of BGF | QUBO solver · P4000(5) | GAP (%): 0.834 -> 0.669 | Helped |
| Use VCM50 instead of BGF | QUBO solver · P5000(5) | GAP (%): 0.914 -> 0.783 | Helped |
| Use VCM50 instead of BGF | QUBO solver · P6000(3) | GAP (%): 0.966 -> 0.806 | Helped |
| Use VCM50 instead of BGF | QUBO solver · P7000(3) | GAP (%): 0.730 -> 0.702 | Helped |
| Extend testing DVN depth from 40 to 300 (ablation) | VCM50-d40 · B2500(10) | GAP (%): 0.362 -> 0.034 | Helped |
| Increase training DVN depth from 10 to 30 (ablation) | VCM50 · B2500(10) | GAP (%): 0.633 -> 0.396 | Helped |
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