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..
Bregman Alternating Direction Method of Multipliers
Authors: Huahua Wang, Arindam Banerjee
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we use BADMM to solve the mass transportation problem [18]: |
| Researcher Affiliation | Academia | Huahua Wang, Arindam Banerjee Dept of Computer Science & Engg, University of Minnesota, Twin Cities EMAIL |
| Pseudocode | No | The paper provides algorithmic steps through mathematical equations (e.g., (7)-(9), (11)-(13)) but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | GPU code is available on https://github.com/anteagle/GPU_BADMM_MT |
| Open Datasets | No | The paper uses the mass transportation problem where data (cost matrix C) is 'randomly generated from the uniform distribution', not a pre-existing, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating data (cost matrix C is randomly generated) but does not provide specific training, validation, or test dataset split percentages or counts. |
| Hardware Specification | Yes | For comparison, BADMM is run in parallel on a Tesla M2070 GPU with 5G memory and 448 cores1. We run Gurobi on two settings: a Mac laptop with 8G memory and a server with 86G memory, respectively. |
| Software Dependencies | No | The paper mentions 'Gurobi' as commercial software and 'LP solvers' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We choose the best parameter for BADMM (ρ = 0.001) and ADMM (ρ = 0.001). The stopping condition is either when the number of iterations exceeds 2000 or when the primal-dual residual is less than 10 4. |