Adaptive Stochastic Alternating Direction Method of Multipliers
Authors: Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Encouraging empirical results on a variety of real-world datasets confirm the effectiveness and efficiency of the proposed algorithms. |
| Researcher Affiliation | Collaboration | Peilin Zhao , ZHAOP@I2R.A-STAR.EDU.SG Jinwei Yang JYANG7@ND.EDU Tong Zhang TZHANG@STAT.RUTGERS.EDU Ping Li PINGLI@STAT.RUTGERS.EDU Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Department of Mathematics, Rutgers University; and Department of Mathematics, University of Notre Dame, USA Department of Statistics & Biostatistics, Rutgers University, USA; and Big Data Lab, Baidu Research, China Department of Statistics & Biostatistics, Department of Computer Science, Rutgers University; and Baidu Research, USA |
| Pseudocode | Yes | Algorithm 1 Adaptive Stochastic Alternating Direction Method of Multipliers (Ada-SADMM). Algorithm 2 Adaptive Stochastic ADMM with Diagonal Matrix Update (Ada-SADMMdiag). Algorithm 3 Adaptive Stochastic ADMM with Full Matrix Update (Ada-SADMMfull). |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | To examine the performance, we test all the algorithms on six real-world datasets from web machine learning repositories, which are listed in the Table 1. The news20 dataset was downloaded from www.cs.nyu.edu/ roweis/data.html. All other datasets were downloaded from the LIBSVM website. |
| Dataset Splits | Yes | For each dataset, we randomly divide it into two folds: training set with 80% of examples and test set with the rest. |
| Hardware Specification | No | All experiments were run in Matlab over a machine of 3.4GHz CPU. This description is not specific enough to identify the hardware model (e.g., Intel Core i7, Xeon), family, or number of cores. |
| Software Dependencies | No | All experiments were run in Matlab over a machine of 3.4GHz CPU. This only mentions 'Matlab' without any version number or specific libraries used. |
| Experiment Setup | Yes | In particular, we set the penalty parameter γ = ν = 1/n, where n is the number of training examples, and the trade-off parameter β = 1. In addition, we set the step size parameter ηt = 1/(γt) for SADMM according to the theorem 2 in (Ouyang et al., 2013). Finally, the smooth parameter a is set as 1, and the step size for adaptive stochastic ADMM algorithms are searched from 2[ 5:5] using cross validation. |