A Proximal Alternating Direction Method for Semi-Definite Rank Minimization
Authors: Ganzhao Yuan, Bernard Ghanem
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that the proposed algorithm outperforms stateof-the-art low-rank semi-definite minimization algorithms in terms of solution quality. |
| Researcher Affiliation | Academia | Ganzhao Yuan and Bernard Ghanem King Abdullah University of Science and Technology (KAUST), Saudi Arabia yuanganzhao@gmail.com, bernard.ghanem@kaust.edu.sa |
| Pseudocode | Yes | Algorithm 1 A Proximal Alternating Direction Method for Solving the Non-Convex MPEC Problem (8) |
| Open Source Code | Yes | We provide our supplementary material and MATLAB implementation online at: http://yuanganzhao.weebly.com/. |
| Open Datasets | Yes | Following the experimental setting in (Biswas et al. 2006b), we uniformly generate c anchors (c = 5 in all our experiments) and u sensors in the range [ 0.5, 0.5] to generate d-dimensional data points. |
| Dataset Splits | No | The paper describes how data points are generated for experiments, but it does not specify any explicit training, validation, or test splits or cross-validation setups. It describes parameters for data generation and noise injection. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions "MATLAB implementation" but does not specify a version number for MATLAB or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | In our experiments, we test the impact of five parameters: d, q, u, s, and r. Although we are mostly interested in ddimensional (d = 2 or 3) localization problems, Problem (3) is also strongly related to Euclidean distance matrix completion, a larger dimension (e.g. d = 7) is also interesting. The range of all these five parameters is summarized in Table 2. Unless otherwise specified, the default parameters in bold are used. Due to space limitation, we only present our experimental localization results in the presence of Gaussian noise (p = 2). For more experimental results on laplace noise (i.e. p = 1) and uniform noise (p = ), please refer to supplementary material. ... dist(S) (1/n n i=1 S(i, :) S(i, :) 2)1/2 where x 0-ϵ is the soft ℓ0 norm which counts the number of elements whose magnitude is greater than a threshold ϵ = 0.01 x , x Rn. |