Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Convergence of Common Proximal Methods for L1-Regularized Least Squares
Authors: Shaozhe Tao, Daniel Boley, Shuzhong Zhang
IJCAI 2015 | Venue PDF | 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 EMAIL |
| 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. |