Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems
Authors: Wenkang Zhan, Gang Wu, Hongchang Gao9006-9013
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we have conducted extensive experiments and the experimental results have confirmed the superior performance of our proposed method. |
| Researcher Affiliation | Collaboration | Wenkang Zhan1, Gang Wu2, Hongchang Gao1 1Department of Computer and Information Sciences, Temple University, PA, USA 2Adobe Research, CA, USA wenkang.zhan@temple.edu, gawu@adobe.com, hongchang.gao@temple.edu |
| Pseudocode | Yes | Algorithm 1: Efficient Decentralized Stochastic Gradient Descent Method (EDSGD) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | In our experiments, we use six classification datasets, which are available at LIBSVM1 and Open ML2. 1https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ 2https://www.openml.org |
| Dataset Splits | No | The paper mentions distributing samples to workers and each worker using its own dataset, but it does not specify explicit training/validation/test splits, percentages, or a cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The regularization coefficient γ is set to 0.001. ... we set the batch size of the first three methods to 256 and DGET to n. As for GT-SARAH and our method, we set it to sqrt(n/K). Similar to (Sun, Lu, and Hong 2020), we set the learning rate to 0.001 for all methods. |