Accelerating Greedy Coordinate Descent Methods
Authors: Haihao Lu, Robert Freund, Vahab Mirrokni
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we present results of our numerical experiments using AGCD and ASCD on synthetic linear regression problems as well as practical logistic regression problems. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics and Operations Research Center, MIT 2Sloan School of Management, MIT 3Google Research. |
| Pseudocode | Yes | Algorithm 1 Accelerated Coordinate Descent Framework without Strong Convexity |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Using this strategy, we solved a large number of logistic regression instances from LIBSVM (Chang & Lin, 2011). |
| Dataset Splits | No | The paper uses datasets like 'synthetic linear regression problems' and 'logistic regression instances from LIBSVM' but does not specify the train/validation/test splits, percentages, or sample counts used for experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using LIBSVM but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | No | The paper states that 'algorithm implementation details are described in the supplementary materials', but the main text does not include specific hyperparameters, optimizer settings, or other detailed experimental setup information. |