Coordinate Descent Methods for Fractional Minimization
Authors: Ganzhao Yuan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world data have shown that our method significantly and consistently outperforms existing methods in terms of accuracy. |
| Researcher Affiliation | Academia | 1Peng Cheng Laboratory, China. Correspondence to: Ganzhao Yuan <yuangzh@pcl.ac.cn>. |
| Pseudocode | Yes | Algorithm 1 Coordinate Descent Methods for Fractional Minimization. |
| Open Source Code | Yes | We provide our Matlab code in the author s research webpage at: https://yuangzh.github.io. |
| Open Datasets | Yes | To generate the design/signal matrix G, we consider four publicly available real-world data sets: e2006tfidf , news20 , sector , and TDT2 . We randomly select a subset of examples from the original data sets (http://www.cad.zju.edu.cn/home/ dengcai/Data/Text Data.html, https://www. csie.ntu.edu.cn/~cjlin/libsvm/). |
| Dataset Splits | No | The paper describes how data is generated and structured for experiments but does not specify explicit train/validation/test splits in percentages or counts. It mentions "randomly select a subset of examples" but not how these are partitioned into distinct training, validation, and test sets. |
| Hardware Specification | Yes | All methods are implemented in MATLAB on an Intel 2.6 GHz CPU with 64 GB RAM. |
| Software Dependencies | No | The paper states "All methods are implemented in MATLAB" and mentions "Matlab inbuilt function roots," but it does not provide specific version numbers for MATLAB or any other software libraries or dependencies. |
| Experiment Setup | Yes | We use the default value (θ, ϵ, υ, T) = (10 6, 10 10, 500, 100). |