A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
Authors: Ran Xin, Usman Khan, Soummya Kar
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided to illustrate our main technical results. In this section, we illustrate our theoretical results on the convergence of the proposed GT-HSGD algorithm with the help of numerical experiments. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA 2Department of Electrical and Computer Engineering, Tufts University, Medford, MA, USA. |
| Pseudocode | Yes | Algorithm 1 GT-HSGD at each node i |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | Data. To test the performance of the applicable decentralized algorithms, we distribute the a9a, covertype, KDD98, Mini Boo NE datasets uniformly over the nodes and normalize the feature vectors such that θi,j = 1, i, j. The statistics of these datasets are provided in Table 2. Table 2. Datasets used in numerical experiments, all available at https://www.openml.org/. |
| Dataset Splits | No | The paper describes using datasets but does not specify explicit train, validation, or test splits, nor does it refer to a standard splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only refers to a "network of n nodes" and "machines or edge devices". |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper discusses parameter tuning procedures (e.g., finding step-size and minibatch size candidate sets) but does not provide the specific hyperparameter values or training configurations used for the final experiments presented in the results. |