Online Convex Optimization Over Erdos-Renyi Random Networks
Authors: Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi
NeurIPS 2020 | Venue 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 EMAIL Peng Yi Tongji University Shanghai, China EMAIL Yiguang Hong Tongji University Shanghai, China EMAIL Jie Chen Tongji University Shanghai, China EMAIL Guodong Shi, The University of Sydney NSW, Australia EMAIL |
| 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. |