Adaptive Three Operator Splitting
Authors: Fabian Pedregosa, Gauthier Gidel
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, an empirical comparison with related methods on 6 different problems illustrates the computational advantage of the proposed method. |
| Researcher Affiliation | Academia | 1University of California at Berkeley, USA 2Department of Computer Science, ETH Zurich, Switzerland 3Mila Universit e de Montr eal, Canada. |
| Pseudocode | Yes | Algorithm 1: Adaptive Three Operator Splitting |
| Open Source Code | No | The paper does not contain an explicit statement that the source code for the described methodology is publicly available, nor does it provide a direct link to such code. While there is a reference to "Pedregosa, F. C-OPT: composite optimization in Python. 2018. doi: 10.5281/zenodo.1283339. URL http://openopt.github.io/copt/", it does not state that the code for this paper's methodology is available there. |
| Open Datasets | Yes | RCV1 and real-sim. Lewis, D. D., Yang, Y., Rose, T. G., and Li, F. RCV1: A new benchmark collection for text categorization research. Journal of machine learning research, 2004. |
| Dataset Splits | No | The paper mentions using datasets like RCV1 and real-sim and discusses regularization parameters, but it does not specify details like train/validation/test split percentages, absolute sample counts for splits, or reference predefined splits with citations. |
| Hardware Specification | No | The paper mentions "Computing time on was donated by Amazon through the program AWS Cloud Credits for Research", but it does not specify any exact GPU/CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Subfigures A and C were run with the regularization parameter chosen to give 50% of sparsity, while B, E are run with higher levels of sparsity, chosen to give 5% of sparsity. For each problem, we show 2 different benchmarks, corresponding to the low and high regularization regimes (denoted low reg and high reg). |