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..
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Authors: Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world data show that our proposed LP solver, ECLIPSE, can solve problems with 1012 decision variables well beyond the capabilities of current solvers. |
| Researcher Affiliation | Collaboration | Kinjal Basu * 1 Amol Ghoting * 1 Rahul Mazumder * 2 Yao Pan * 1 *Equal contribution 1Linked In Corporation 2MIT. Correspondence to: Kinjal Basu <EMAIL>, Rahul Mazumder <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 ECLIPSE: Extreme-Scale LP Solver |
| Open Source Code | No | We will soon open-source the solver. |
| Open Datasets | No | We randomly simulate the vectors c, p and b, and solve the problem: ... Note that, since we could not ο¬nd open-source example datasets that exactly ο¬t the problem structure, we excluded comparisons with open-source LP instances. |
| Dataset Splits | No | The paper mentions 'convergence of our method as iterations increase' and 'relative duality gap as the stopping criteria' but does not explicitly provide training, validation, or test dataset splits. |
| Hardware Specification | No | The experiment was running in a development cluster in Spark 2.3 with up to 800 executors. The paper does not provide specific hardware details such as CPU/GPU models, memory, or processor types. |
| Software Dependencies | Yes | We built the system on top of Apache Spark (Zaharia et al., 2016) ... The experiment was running in a development cluster in Spark 2.3 with up to 800 executors. |
| Experiment Setup | No | The paper discusses concepts like the regularization parameter 'Ξ³' and 'step-size' and mentions 'a constant step-size in our exposition,' but it does not provide concrete numerical values for these or other hyperparameters (e.g., learning rates, batch sizes, epochs) used in their experiments. |