Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Authors: Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the efficiency of APGnc and SVRG-APGnc with other competitive methods via numerical experiments. |
| Researcher Affiliation | Academia | 1Syracuse University, NY, USA. Correspondence to: Qunwei Li <qli33@syr.edu>. |
| Pseudocode | Yes | Algorithm 1 APG |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about code availability for the described methodology. |
| Open Datasets | No | For the experiment, we set n = 2000, γ = 10^-3 and randomly generate the samples zi from normal distribution. All samples are then normalized to have unit norm. The initialization is randomly generated, and is applied to all the methods. The paper does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We tuned a fixed step size η = 0.05/L, where L is the spectral norm of the sample matrix n P i=1 ziz T i . We set t = 1/2 for APGnc+. ... and pick the stepsize η = 1/8m L with m = n. |