Online Convex Optimization Over Erdos-Renyi Random Networks
Authors: Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical studies have validated the theoretical findings. |
| Researcher Affiliation | Academia | Jinlong Lei Tongji University Shanghai, China leijinlong@tongji.edu.cn Peng Yi Tongji University Shanghai, China yipeng@tongji.edu.cn Yiguang Hong Tongji University Shanghai, China yghong@iss.ac.cn Jie Chen Tongji University Shanghai, China chenjie@bit.edu.cn Guodong Shi, The University of Sydney NSW, Australia guodong.shi@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Distributed online gradient descent with full gradients; Algorithm 2 Distributed online algorithm with one-point bandit feedback; Algorithm 3 Distributed algorithm with two-points bandit feedback. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | we examine the empirical performance of the proposed distributed algorithms on the bodyfat dataset with 14 features and 252 instances from LIBSVM library 2. The data set is from https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper uses the 'bodyfat dataset' but does not specify any training, validation, or test splits by percentages, sample counts, or predefined methods. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide any specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We set N = 30, p = 0.2, and µ = 0 in (11) to get convex losses. In addition, we set µ = 1 in (11) to construct strongly convex losses. We fix the time horizon T = 200. Let the link connection probability p vary from p = 0.1 to p = 0.9 at a stride of 0.1. We set N = 20, let the vector dimension d vary from d = 5 to d = 100. |