Distributed Accelerated Proximal Coordinate Gradient Methods
Authors: Yong Ren, Jun Zhu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the regularized empirical risk minimization problem demonstrate the effectiveness of our algorithm and match our theoretical findings. |
| Researcher Affiliation | Academia | Yong Ren, Jun Zhu Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems Dept. of Comp. Sci. & Tech., TNList Lab, Tsinghua University renyong15@mails.tsinghua.edu.cn; dcszj@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 The Dis APCG algorithm. Algorithm 2 Dis APCG without full-dimensional vector operators in the case µκ > 0 Algorithm 3 Dis APCG for regularized ERM with µ > 0 |
| Open Source Code | No | The paper states 'We implement the algorithms by C++ and open MPI' but does not provide any link or explicit statement about making their code available. |
| Open Datasets | Yes | Experiments are performed on 3 datasets from [Fan and Lin, 2011] whose information is summarized in Table 1. [Fan and Lin, 2011] is cited as 'Libsvm data: Classification, regression and multi-label. URL: http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets, 2011.' |
| Dataset Splits | No | The paper mentions using datasets but does not provide specific details on how the data was split into training, validation, or test sets (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper states: 'We implement the algorithms by C++ and open MPI and run them in clusters on Tianhe-II super computer, where in each node we use a single cpu.' While 'Tianhe-II super computer' is mentioned, it lacks specific CPU/GPU models, memory, or more detailed system specifications. |
| Software Dependencies | No | The paper mentions 'C++ and open MPI' as implementation details but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We either vary the mini-batch size τ on each node with the number of nodes K fixed, or vise versa. For a fair comparison, we set the mini-batch size to be τ = 102 for our Dis APCG method and Dis DCA. We vary λ from 10 6 to 10 8, which is a relatively hard setting since the strong convexity parameter is small. For all settings, we use K = 16 nodes. |