Efficient
Authors: k
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical results in learning tasks including logistic regression and matrix pursuit demonstrate the substantially improved computational efficiency of our algorithm over the state-of-the-art proximal gradient algorithms. |
| Researcher Affiliation | Academia | Department of Computer Science, Rutgers, The State University of New Jersey Jiangsu Province Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology Department of Computer Science, University of North Carolina at Charlotte |
| Pseudocode | Yes | Algorithm 1: k-FCFW Algorithm for k-support-norm regularized problem. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | The MNIST [Le Cun et al., 1998] and USPS [Hull, 1994] datasets are adopted for testing. |
| Dataset Splits | Yes | λ is selected by grid search according to the testing result on a validation set of size 100. |
| Hardware Specification | No | All the considered algorithms are implemented in Matlab and tested on a computer equipped with 3.0GHz CPU and 32GB RAM. |
| Software Dependencies | No | All the considered algorithms are implemented in Matlab. No specific version numbers for Matlab or other software dependencies are provided. |
| Experiment Setup | Yes | We produce the training data by setting M = 500, p = 10^6, g = 20, p0 = 10000, k = 2000, 4000, 6000, 8000 and 10000, respectively. λ is selected by grid search according to the testing result on a validation set of size 100. We set the termination criterion as |F (w(t)) F (w(t 1))| F (w(t 1)) 10^-4. |