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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LIMIP: Lifelong Learning to Solve Mixed Integer Programs
Authors: Sahil Manchanda, Sayan Ranu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate LIMIP on a series of NP-hard problems and establish that in comparison to existing baselines, LIMIP is up to 50% better when confronted with lifelong learning. |
| Researcher Affiliation | Academia | Sahil Manchanda and Sayan Ranu Department of Computer Science and Engineering Indian Institute of Technology, Delhi EMAIL |
| Pseudocode | Yes | The detailed steps of training a sequence of tasks in lifelong fashion through LIMIP are described in Algorithm 1 in Appendix A.4. |
| Open Source Code | Yes | The codebase can be found on https://github.com/ideaiitd/Li MIP . |
| Open Datasets | No | The paper describes generating its own datasets based on known problems like Set Cover and Independent Set, but does not provide concrete access information (link, DOI, specific citation with authors/year) for publicly available versions of the exact datasets used for training. For example, it cites Balas and Ho (1980) for Set Cover, which defines the problem, not a specific dataset instance used for training. |
| Dataset Splits | Yes | For each task, we generate 150,000 branching samples extracted using 10,000 generated instances for training and 30000 validation/test samples generated using 2000 instances. |
| Hardware Specification | Yes | We use a system running on Intel Xeon 6248 processor with 96 cores and 1 NVIDIA A100 GPU with 40GB memory for our experiments. |
| Software Dependencies | Yes | We use SCIP (Gamrath et al. 2020) as the backend solver... Gamrath, G.; Anderson, D.; Bestuzheva, K.; Chen, W.-K.; Eifler, L.; Gasse, M.; Gemander, P.; Gleixner, A.; Gottwald, L.; and Halbig, K. 2020. The SCIP Optimization Suite 7.0. ZIB-Report. |
| Experiment Setup | Yes | 4.2 Experimental Setup and Parameters ... We use attention mechanism with 2 heads. We set the default buffer size to 500. For details of all parameters and system settings, we refer to App A.6. |