Accelerated Proximal Gradient Methods for Nonconvex Programming
Authors: Huan Li, Zhouchen Lin
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 lihuanss@pku.edu.cn zlin@pku.edu.cn |
| 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. |