Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Decentralized Sum-of-Nonconvex Optimization
Authors: Zhuanghua Liu, Bryan Kian Hsiang Low
AAAI 2024 | Venue PDF | 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. |