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..
A Conditional-Gradient-Based Augmented Lagrangian Framework
Authors: Alp Yurtsever, Olivier Fercoq, Volkan Cevher
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents the numerical evidence to demonstrate the empirical superiority of CGAL, based on the max-cut, clustering ,and generalized eigenvector problems. |
| Researcher Affiliation | Academia | 1LIONS, Ecole Polytechnique F ed erale de Lausanne, Switzerland 2LTCI, T el ecom Paris Tech, Universit e Paris-Saclay, France. |
| Pseudocode | Yes | Algorithm 1 CGAL (for g(Bx) = 0) and Algorithm 2 CGAL for (P) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is released or available. |
| Open Datasets | Yes | We use the GD97 b dataset1... 1V. Batagelj and A. Mrvar. Pajek datasets, http://vlado. fmf.uni-lj.si/pub/networks/data/ ; We consider a medium scale experiment, where we compare CGAL, HCGM, and UPD for max-cut with G1 (800 800) and G40 (2000 2000) datasets2... 2Y. Ye. Gset random graphs. https://www.cise.ufl. edu/research/sparse/matrices/gset/ ; We use the same setup as in (Yurtsever et al., 2018), which is designed and published online by Mixon et al. (2017). This setup contains a 1000 1000 dimensional dataset generated by sampling and preprocessing the MNIST dataset3 using a one-layer neural network. Further details on this setup and the dataset can be found in (Mixon et al., 2017). 3Y. Le Cun and C. Cortes. MNIST handwritten digit database, http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper mentions using specific datasets and tuning parameters, but it does not provide specific details on train/validation/test dataset splits, such as percentages, sample counts, or citations to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions tuning parameters like the penalty parameter λ0 and accuracy parameter ϵ, but it does not provide specific hyperparameter values, training configurations, or system-level settings in the main text. |