Convergence of Common Proximal Methods for L1-Regularized Least Squares
Authors: Shaozhe Tao, Daniel Boley, Shuzhong Zhang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Examples We consider examples of compressed sensing to show different convergence behaviors to support our analysis. ... The numerical results are summarized in Table 1. ... Fig. 1 illustrates the methods behavior for the instance marked in Table 1, to show how the theorems established before explains the behaviors in practice. |
| Researcher Affiliation | Academia | Shaozhe Tao, Daniel Boley, Shuzhong Zhang University of Minnesota, Minneapolis MN 55455 USA {taoxx120,boley,zhangs}@umn.edu |
| Pseudocode | Yes | Algorithm 1: One pass of modified ADMM |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data ('We let A Rm n be Gaussian matrix whose elements are i.i.d distributed as N(0, 1), ϵ be a vector whose elements are i.i.d distributed as N(0, σ2) with σ = 10 3.') rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes synthetic data generation but does not provide specific training/validation/test dataset splits, percentages, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Problem Setting m = 64, n = 512 s = 7, λ = 0.3... We let A Rm n be Gaussian matrix whose elements are i.i.d distributed as N(0, 1), ϵ be a vector whose elements are i.i.d distributed as N(0, σ2) with σ = 10 3. |