Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning to Schedule Heuristics in Branch and Bound
Authors: Antonia Chmiela, Elias Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive computational experiments on a class of challenging instances and demonstrate the benefits of our approach (Section 6). |
| Researcher Affiliation | Academia | Antonia Chmiela Zuse Institute Berlin, Germany EMAIL Elias B. Khalil University of Toronto, Canada EMAIL Ambros Gleixner Zuse Institute Berlin, Germany HTW Berlin, Germany EMAIL Andrea Lodi CERC, Polytechnique Montréal, Canada EMAIL Sebastian Pokutta Zuse Institute Berlin, Germany Technische Universität Berlin, Germany EMAIL |
| Pseudocode | Yes | The final algorithm can be found in Appendix C. |
| Open Source Code | Yes | The code we use for data collection and scheduling is publicly available.1 [1https://github.com/antoniach/heuristic-scheduling] |
| Open Datasets | Yes | The Generalized Independent Set Problem (GISP) (Hochbaum and Pathria, 1997; Colombi et al., 2017) and the Fixed-Charge Multicommodity Network Flow Problem (FCMNF) (Hewitt et al., 2010). For GISP, we generate two types of instances: The first one takes graphs from the 1993 DIMACS Challange which is also used by Khalil et al. (2017) and Colombi et al. (2017) |
| Dataset Splits | No | The paper provides training and testing set sizes (e.g., “120 for training and testing,” “25 for training and 10 for testing,” “20 for training and 120 for testing”) but does not specify a separate validation set or detailed split methodology (e.g., percentages, random seed, or cross-validation setup) that would allow for exact reproduction of data partitioning. |
| Hardware Specification | Yes | For our experiments, we used a Linux cluster of Intel Xeon CPU E5-2660 v3 2.60GHz with 25MB cache and 128GB main memory. |
| Software Dependencies | Yes | used the state-of-the-art solver SCIP 7.0 (Gamrath et al., 2020) with CPLEX 12.10.0.0 as the underlying LP solver. |
| Experiment Setup | Yes | The time limit in all experiments was set to two hours; for data collection to four hours. [...] we implemented an exhaustive testing framework that uses four random seeds [...] We used the primal integral as a performance metric. [...] We set the frequency offset to 0 for all diving heuristics. |