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Benchmarks
Polynomial-Objective Integer Programming on RandQCP 1k
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0.32
Gap (%)
HNN
-1.22
9.175
19.57
29.965
Mar 16, 2026
Gap (%)
Updated 2mo ago
Evaluation Results
Method
Method
Links
Gap (%)
HNN
Train=Mini, Base solve...
2026.03
0.32
HNN
Train=1000, Base solve...
2026.03
0.43
HNN
Train=Mini, Base solve...
2026.03
0.46
NeuralQP
Train=Mini, Base solve...
2026.03
0.47
HNN
Train=1000, Base solve...
2026.03
0.5
NeuralQP
Train=1000, Base solve...
2026.03
0.54
TriGNN
Train=Mini, Base solve...
2026.03
0.55
TriGNN
Train=1000, Base solve...
2026.03
0.57
NeuralQP
Train=Mini, Base solve...
2026.03
0.6
NeuralQP
Train=1000, Base solve...
2026.03
0.62
TriGNN
Train=Mini, Base solve...
2026.03
0.67
TriGNN
Train=1000, Base solve...
2026.03
0.72
Exact solver
Train=-, Base solver=G...
2026.03
3.21
GNNQP
Train=1000, Base solve...
2026.03
3.48
GNNQP
Train=Mini, Base solve...
2026.03
3.53
GNNQP
Train=Mini, Base solve...
2026.03
3.6
GNNQP
Train=1000, Base solve...
2026.03
3.62
Exact solver
Train=-, Base solver=SCIP
2026.03
38.82
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