Curriculum learning for multilevel budgeted combinatorial problems
Authors: Adel Nabli, Margarida Carvalho
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results close to optimality on graphs up to 100 nodes and a 185 speedup on average compared to the quickest exact solver known for the Multilevel Critical Node problem, a max-min-max trilevel problem that has been shown to be at least Σp 2-hard. ... Section 6 Computational Results |
| Researcher Affiliation | Academia | Adel Nabli Margarida Carvalho CIRRELT and Département d Informatique et de Recherche Opérationnelle Université de Montréal adel.nabli@umontreal.ca carvalho@iro.umontreal.ca |
| Pseudocode | Yes | See Appendix B.1 for the pseudo-code. ... Algorithm 1: Multi L-Cur ... The pseudo-code for the Greedy Rollout function is available in Appendix B.2 ... Its pseudo-code is available in Appendix B.3. |
| Open Source Code | Yes | We make our code publicly available: https://github.com/Adel Nabli/MCN |
| Open Datasets | Yes | MCN [1] along with a publicly available dataset of solved instances2, which we can use to assess the quality of our heuristic. ... 2https://github.com/mxmmargarida/Critical-Node-Problem ... The first distribution of instances considered is D(1), constituted of Erdos-Renyi graphs [18] with size |V |(1) [[10, 23]] and arc density d(1) [0.1, 0.2]. ... The second distribution of instances D(2) focused on larger graphs with |V |(2) [[20, 60]], d(2) [0.05, 0.15]. |
| Dataset Splits | No | Algorithm 1 mentions 'Create Dj train, Dj val by sampling' and 'validation set' (Lj val) but does not provide specific details on the split percentages, absolute counts, or detailed splitting methodology. |
| Hardware Specification | Yes | To train our agent and at inference, we used one gpu of a cluster of NVidia V100SXM2 with 16G of memory4. |
| Software Dependencies | Yes | The architectures presented in Figure 2 was implemented with Pytorch Geometric [21] and Pytorch 1.4 [50]. |
| Experiment Setup | No | The paper mentions using 'Adam [35]' as the optimizer but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed optimizer settings in the main text. It refers to Appendix D for 'Further details of the implementation', but such details are not explicitly present in the main body. |