Curvature-Exploiting Acceleration of Elastic Net Computations
Authors: Vien Mai, Mikael Johansson
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform numerical experiments to verify the efficacy of the proposed method on real world data sets (Chang & Lin, 2011; Guyon et al., 2008). |
| Researcher Affiliation | Academia | 1Department of Department of Automatic Control, School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), Stockholm, Sweden. |
| Pseudocode | Yes | Algorithm 1 Randomized Block Lanczos Method (...) Algorithm 2 Inexact Accelerated Scaled Proximal SVRG |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | In this section, we perform numerical experiments to verify the efficacy of the proposed method on real world data sets (Chang & Lin, 2011; Guyon et al., 2008). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like FISTA, Prox-SVRG, Katyusha1, and BCD, but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | For each algorithm above, we tune only the step size, from the set η {10k, 2 10k, 5 10k|k {0, 1, 2}}, where η is the theoretical step size, and report the one having smallest objective value. Other hyper-parameters are set to their theory-predicted values. All methods are initialized at 0. For the subproblems in Algorithm 2, we just simply run FISTA with κsub log κsub iterations as discussed previously, without any further tunning steps. The value of r is chosen as a small fraction of d so that the preprocessing time of Algorithm 1 is negligible. |