Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity
Authors: Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform simulation experiments to verify the performance of our proposed algorithms. 5.1. Experimental Settings 5.2. Experimental Results |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 24Paradigm Inc., Beijing, China. |
| Pseudocode | Yes | Algorithm 1 CGSC; Algorithm 2 D-BOCG; Algorithm 3 D-BBCG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | As in Zhang et al. (2017), we also use the aloi dataset from the LIBSVM repository (Chang & Lin, 2011), the details of which are summarized in Table 1. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments are provided. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | According to Zhang et al. (2017), we set the bound of trace norm as τ = 50, and set st = 1/ t and η = c T 3/4 for D-OCG by tunning the constant c. For our D-BOCG, we set K = T , ϵ = 1e 5, L = 20 and η = c T 3/4 by tunning the constant c. For both D-BOCG and D-OCG, the constant c is selected from [0.01, , 1e5]. ... For D-BBCG, we set K = T , ϵ = 1e 5, L = 20, δ = 0.1 and η = c T 3/4 by selecting the constant c from [0.01, , 1e5]. |