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 Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Authors: Sulaiman Alghunaim, Kun Yuan, Ali H. Sayed
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We remark that simulations of the proposed algorithm are provided in Section B in the supplementary material. |
| Researcher Affiliation | Academia | Sulaiman A. Alghunaim, Kun Yuan Electrical and Computer Engineering Department University of California Los Angeles Los Angeles, CA, 90095 EMAIL Ali H. Sayed Ecole Polytechnique Fédérale de Lausanne CH-1015 Lausanne, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm (Proximal Primal-Dual Diffusion P2D2) Setting: Let B = 0.5(I A) = [bsk] and choose step-sizes µ and α. Set all initial variables to zero and repeat for i = 1, 2,... |
| Open Source Code | No | The paper does not provide any concrete access (link or explicit statement) to the source code for the methodology described. |
| Open Datasets | No | The paper mentions simulations but does not provide any information about specific datasets used or their public availability. |
| Dataset Splits | No | The paper does not provide information on dataset splits (train, validation, test). |
| Hardware Specification | No | The paper does not specify any hardware details (GPU/CPU models, memory, etc.) used for running experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper defines tunable step-sizes µ and α within the algorithm, but it does not provide specific hyperparameter values, training configurations, or system-level settings within an experimental setup description. |