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DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use discrete (Bernoulli) diffusion instead of continuous (Gaussian) diffusion (ablation) | DIFUSCO · TSP-100, 50 steps x 1 sample, w/o 2-opt | gap (%): 7.66379 -> 1.45574 | Helped |
| use cosine instead of linear inference schedule (ablation) | DIFUSCO discrete diffusion · TSP-50 | gap (%) | Helped |
| use cosine instead of linear inference schedule (ablation) | DIFUSCO continuous diffusion · TSP-50 | gap (%) | No clear effect |
| more diffusion steps instead of more samples (ablation) | DIFUSCO · TSP-50 | gap (%) | Helped |
| add 2-opt post-processing to greedy decoding (ablation) | DIFUSCO · TSP-500 | gap: 10.85% -> 1.49% | Helped |
| add 2-opt post-processing to greedy decoding (ablation) | DIFUSCO · TSP-10000 | gap: 36.75% -> 3.10% | Helped |
| use more MCTS heatmap samples (1x to 4x) (ablation) | DIFUSCO MCTS · TSP-500 | gap: 0.46% -> 0.02% | Helped |
| use more MCTS heatmap samples (1x to 4x) (ablation) | DIFUSCO MCTS · TSP-10000 | gap: 2.58% -> 2.48% | Helped |
| more diffusion steps and samples together (50x1 to 50x16) (ablation) | DIFUSCO discrete diffusion · TSP-100 w/ 2opt | gap (%): 0.23869 -> -0.02322 | Helped |
| DIFUSCO (SL+MCTS) vs prior best DIMES (RL+AS+MCTS) | large-scale TSP · TSP-500 | gap: 1.76% -> 0.46% | Helped |
| DIFUSCO on ER-[700-800] MIS graphs | DIFUSCO · ER-[700-800] | MIS performance | Hurt |
Closest papers by meaning
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