Linearized Alternating Direction Method with Penalization for Nonconvex and Nonsmooth Optimization
Authors: Yiyang Wang, Risheng Liu, Xiaoliang Song, Zhixun Su
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of our proposed algorithm, experiments with synthetic and real-world data have been conducted on specific applications in signal and image processing. |
| Researcher Affiliation | Academia | Yiyang Wang,1 Risheng Liu,2 Xiaoliang Song,1 and Zhixun Su1,3 1 School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China 2 School of Software Technology, Dalian University of Technology, Dalian 116024, China 3 National Engineering Research Center of Digital Life, Guangzhou 510006, China |
| Pseudocode | Yes | Algorithm 1 Solving problem (3) by LADMP |
| Open Source Code | No | The paper does not provide any links to open-source code or explicit statements about code availability. |
| Open Datasets | No | The paper describes generating synthetic data and using images like 'Barbara' and 'Pepper' for denoising, but it does not provide concrete access information (e.g., specific links, DOIs, repositories, or formal citations with authors/years) for these datasets to be considered publicly available or open for replication. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | All the algorithms, including comparative methods are implemented by Matlab R2013b and are tested on a PC with 8 GB of RAM and Intel Core i5-4200M CPU. |
| Software Dependencies | Yes | All the algorithms, including comparative methods are implemented by Matlab R2013b |
| Experiment Setup | Yes | Other parameters of LADMP are empirically set as: μ0 = 0.17, ν = 0.1, η1 k = 10 5, η2 k = μk 10 5 and all the algorithms are stopped when xk+1 xk / xk < 10 7. ... All the parameters of LADMP are set as: λ = 1.0, γ = 0.4, μ0 = 0.2, η1 k = 10 5 and η2 k = μk for dealing with all the images. |