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..
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Authors: Simon Lacoste-Julien, Martin Jaggi
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of the presented algorithm variants in two numerical experiments, shown in Figure 2. |
| Researcher Affiliation | Academia | Simon Lacoste-Julien INRIA SIERRA project-team Ecole Normale Sup erieure, Paris, France Martin Jaggi Dept. of Computer Science ETH Z urich, Switzerland |
| Pseudocode | Yes | Algorithm 1 Away-steps Frank-Wolfe algorithm: AFW(x(0), A, ϵ) |
| Open Source Code | No | Code is available from the authors website. |
| Open Datasets | Yes | For the LMOA, we re-use the code provided by [16] and their included aeroplane dataset resulting in a QP over 660 variables. |
| Dataset Splits | No | Not found. The paper does not specify exact train/validation/test split percentages, sample counts, or cross-validation setup for the datasets used. |
| Hardware Specification | No | Not found. The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for the experiments. |
| Software Dependencies | No | Not found. The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | No | Not found. The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size) or explicit training configurations. |