Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization
Authors: Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, experimental results underscore the empirical advantages of our proposed algorithms when applied to real-world datasets. |
| Researcher Affiliation | Academia | 1School of Information Management and Engineering, Shanghai University of Finance and Economics 2School of Data Science, Fudan University |
| Pseudocode | Yes | Algorithm 1: DGFM at each node i |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology was found. |
| Open Datasets | Yes | Data: We evaluate our proposed algorithms using several standard datasets in LIBSVM (Chang and Lin 2011), which are described in Table 1. |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly provide details about validation dataset splits (percentages, counts, or explicit standard validation sets). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, cloud instance types) used for running experiments were provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Throughout all the experiments, we set δ = 0.001 and tune the stepsize η from {0.0005, 0.001, 0.005, 0.01} for all four algorithms and b from {10, 100, 500}, T from {10, 50, 100} for DGFM+ and GFM+, T from {1, 5, 10} for DGFM+, m = 20 for two decentralized algorithms. ... Throughout all the experiments, we set δ = 0.01, b = {16, 32, 64}. For DGFM+ and GFM+, we tune b from {40, 80, 800, 1600}, T from {2, 5, 10, 20}. Additionally, tune T from {1, 10, 20} for DGFM+. For all algorithms, we tune the stepsize η from {0.05, 0.1, 0.5, 1} and multiply a decay factor 0.6 if no improvement in 300 iterations. For all experiments, we set the initial perturbation as 0. |