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..
Accelerated Proximal Gradient Methods for Nonconvex Programming
Authors: Huan Li, Zhouchen Lin
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we test the performance of our algorithm on the problem of Sparse Logistic Regression (LR)... We test the performance on the real-sim data set... The result is reported in Table 2. We also plot the curves of objective function values vs. iteration number and CPU time in Figure 1. |
| Researcher Affiliation | Academia | Huan Li Zhouchen Lin B Key Lab. of Machine Perception (MOE), School of EECS, Peking University, P. R. China Cooperative Medianet Innovation Center, Shanghai Jiaotong University, P. R. China EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Monotone APG and Algorithm 2 Nonmonotone APG |
| Open Source Code | No | The paper does not explicitly state that the source code for the proposed methods (monotone APG, nonmonotone APG) is available or provide a link to it. |
| Open Datasets | Yes | We test the performance on the real-sim data set9...9http://www.csie.ntu.edu.cn/~cjlin/libsvmtools/datasets |
| Dataset Splits | No | The paper states 'We randomly choose 90% of the data as training data and the rest as test data' but does not specify a separate validation split or explicit proportions for it. |
| Hardware Specification | Yes | All algorithms are run on Matlab 2011a and Windows 7 with an Intel Core i3 2.53 GHz CPU and 4GB memory. |
| Software Dependencies | Yes | All algorithms are run on Matlab 2011a |
| Experiment Setup | Yes | We follow [19] to set λ = 0.0001, θ = 0.1λ and the starting point as zero vectors. In nm APG we set η = 0.8. In IFB the inertial parameter β is set at 0.01 and the Lipschitz constant is computed by backtracking. |