Research map / Probabilistic and scientific modeling / Variational and generative sampling
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DIMES RL+AS+MCTS rather than Att-GCN | TSP solver · TSP-500 | Length: 16.97 -> 16.84 | Helped |
| Use DIMES RL+AS+MCTS rather than Att-GCN | TSP solver · TSP-1000 | Length: 23.86 -> 23.69 | Helped |
| Use DIMES RL+AS+MCTS rather than Att-GCN | TSP solver · TSP-10000 | Length: 74.93 -> 74.06 | Helped |
| Use DIMES RL+AS+MCTS rather than LKH-3 (default) | TSP solver · TSP-10000 | Length: 71.77 -> 74.06 | Hurt |
| Use DIMES RL+S rather than LwD | MIS solver · SATLIB | Size: 422.22 -> 423.28 | Helped |
| Use DIMES RL+S rather than LwD | MIS solver · ER-[700-800] | Size: 41.17 -> 42.06 | Helped |
| Use DIMES RL+S rather than LwD | MIS solver · ER-[9000-11000] | Size: 345.88 -> 332.80 | Hurt |
| Add inner updates and inference fine-tuning (ablation) | DIMES on TSP-1000 · TSP-1000 | Length: 27.11 -> 24.91 | Helped |
| Add inner updates without inference fine-tuning (ablation) | DIMES on TSP-1000 · TSP-1000 | Length: 27.11 -> 26.58 | Helped |
| Add inference fine-tuning without inner updates (ablation) | DIMES on TSP-1000 · TSP-1000 | Length: 27.11 -> 25.68 | Helped |
| Fine-tune GNN output and MLP rather than all GNN and MLP parameters (ablation) | DIMES on TSP-1000 · TSP-1000 | Length: 26.81 -> 26.49 | Helped |
| Increase inner update steps from T=0 to T=14 (ablation) | DIMES on TSP-1000 · TSP-1000 | Length: 25.79 -> 24.91 | Helped |
Closest papers by meaning
- Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method (NeurIPS 2023 · similarity 0.91)
- Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization (ICML 2025 · similarity 0.89)
- Preference Optimization for Combinatorial Optimization Problems (ICML 2025 · similarity 0.89)
- Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation (AAAI 2025 · similarity 0.89)
- Large Language Models as End-to-end Combinatorial Optimization Solvers (NeurIPS 2025 · similarity 0.88)
- Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization (ICML 2024 · similarity 0.88)
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