Deterministic Symmetric Positive Semidefinite Matrix Completion
Authors: William E Bishop, Byron M. Yu
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the algorithm s utility on noiseless and noisy simulated datasets. We demonstrate our algorithm s performance on simulated data, starting with the noiseless setting in Fig. 2. |
| Researcher Affiliation | Academia | Carnegie Mellon University {wbishop, byronyu}@cmu.edu |
| Pseudocode | Yes | The pseudocode for this algorithm is given in Algorithm 1. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that the code will be made available. |
| Open Datasets | No | The paper describes generating simulated data ("randomly generating a C Rn r with entries individually drawn from a N(0, 1) distribution and forming A as A = CCT"), rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes experiments on simulated data, and does not provide specific train/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper refers to MATLAB indexing notation, implying its use, but does not provide specific software dependencies with version numbers (e.g., "MATLAB R2020a" or "Python 3.8, NumPy 1.20"). |
| Experiment Setup | Yes | In all of the noiseless simulations, we simulate a rank r matrix A Sn + by first randomly generating a C Rn r with entries individually drawn from a N(0, 1) distribution and forming A as A = CCT. We use a block diagonal mask with 25 25 blocks and an overlap of 15. |