Bregman Alternating Direction Method of Multipliers
Authors: Huahua Wang, Arindam Banerjee
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 {huwang,banerjee}@cs.umn.edu |
| 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. |