Parallel Direction Method of Multipliers
Authors: Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of PDMM in solving robust principal component analysis (RPCA) and overlapping group lasso [28]. We compared PDMM with ADMM [2] or GSADMM (no theory guarantee), s ADMM [17, 26], and RBSUMM [14]. All experiments are implemented in Matlab and run sequentially. We run the experiments 10 times and report the average results. |
| Researcher Affiliation | Academia | Huahua Wang , Arindam Banerjee , Zhi-Quan Luo University of Minnesota, Twin Cities {huwang,banerjee}@cs.umn.edu, luozq@umn.edu |
| Pseudocode | No | The paper describes algorithmic steps using mathematical equations (e.g., 5-7) but does not present them within a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper mentions 'All experiments are implemented in Matlab' but does not provide any specific link or statement about the open-source availability of the code for the described methodology. |
| Open Datasets | Yes | A = L + S + V is generated in the same way as [17]1. ... 1http://www.stanford.edu/~boyd/papers/prox_algs/matrix_decomp.html |
| Dataset Splits | No | The paper describes the data generation process and stopping criteria for experiments, but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper states 'All experiments are implemented in Matlab and run sequentially' but provides no specific details about the hardware (e.g., CPU/GPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper states 'All experiments are implemented in Matlab' but does not specify a version number for Matlab or any other ancillary software dependencies. |
| Experiment Setup | Yes | In PDMM, ρ = 1 and τi, νi are chosen according to (8), i.e., (τi, νi) = {(1/3, 1/3), (1/6, 1/6), (1/9, 1/9)} for PDMM1, PDMM2 and PDMM3 respectively. We choose the best results for GSADMM (ρ = 1) and RBSUMM (ρ = 1, α = ρ^(1/(t+10))) and s ADMM (ρ = 1). The stopping criterion is either when the residual is smaller than 10^-4 or when the number of iterations exceeds 2000. |