Decentralized Optimization with Edge Sampling
Authors: Chi Zhang, Qianxiao Li, Peilin Zhao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These theoretical findings are validated by both numerical experiments on the mixing rates of Markov Chains and distributed machine learning problems. We have theoretically shown the DDA-ES algorithm achieves of goal of reducing the communication cost on each round while accelerating the overall convergence rates under the same communication budget, and we shall validate our findings with numerical experiments in this part. |
| Researcher Affiliation | Collaboration | 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore 2 IHPC, Agency for Science, Technology and Research, Singapore 3Tencent AI Lab, China |
| Pseudocode | Yes | Algorithm 1 Distributed Dual Averaging with Edge Sampling (DDA-ES) |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of the source code for the methodology described. |
| Open Datasets | Yes | We now consider a distributed optimization problem for the a9a dataset3. 3https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper references the 'a9a dataset' but does not provide specific details on the train, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | For all algorithms, we set ηt = O(1/√t) as suggested in the previous theoretical analysis. with proximal function in Eq (3) set as ψ(w) = λ/2 ||w||^2 and λ = 10^-3. |