Decentralized Sum-of-Nonconvex Optimization
Authors: Zhuanghua Liu, Bryan Kian Hsiang Low
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments validate the theoretical guarantee of our proposed algorithms on both synthetic and real-world datasets. Numerical experiments on several synthetic and realworld datasets demonstrate significant improvement of our proposed PMGT-Katyusha X over existing baseline methods. To demonstrate the efficiency of PMGT-Katyusha X, we evaluate the proposed method on the sub-problem of solving PCA by the shift-and-invert method. ... We conduct our experiments on both synthetic and realworld datasets. |
| Researcher Affiliation | Collaboration | Zhuanghua Liu1, 2, Bryan Kian Hsiang Low1 1Department of Computer Science, National University of Singapore 2CNRS@CREATE LTD, 1 Create Way, #08-01 CREATE Tower, Singapore 138602 |
| Pseudocode | Yes | Algorithm 1: PMGT-Katyusha X; Algorithm 2: Fast Mix(x0, M, W) |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code open or providing a link to a code repository. |
| Open Datasets | Yes | For the real-world dataset, we use the Covtype downloaded from the LIBSVM website2. 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes an optimization problem evaluated on given datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | The left column represents results with the ratio r = 2 and the right column represents results with the ratio r = 300 defined in Problem (3). ... The gossip matrix W underlying the decentralized network... The second largest eigenvalue of the resulting matrix is λ2(W) = 0.97. |