Blind Deconvolutional Phase Retrieval via Convex Programming
Authors: Ali Ahmed, Alireza Aghasi, Paul Hand
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we provide an ADMM implementation of the method and provide numerical experiments that verify the theory. Numerical experiments show that, using this algorithm, one can successfully recover a blurred image from the magnitude only measurements of its Fourier spectrum. |
| Researcher Affiliation | Academia | Ali Ahmed Department of Electrical Engineering Information Technology University Lahore, Pakistan. ali.ahmed@itu.edu.pk Alireza Aghasi Department of Business Analytics Georgia State University Atlanta, GA. aaghasi@gsu.edu Paul Hand College of Computer and Information Science Northeastern University Boston, MA. p.hand@northeastern.edu |
| Pseudocode | No | The paper refers to an ADMM implementation and moving technical details to supplementary material, but does not include a structured pseudocode or algorithm block within the provided text. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide any links to a code repository. |
| Open Datasets | No | The paper describes generating data based on "standard Gaussian matrices" and testing different combinations of parameters (n and k), but it does not mention or provide access to a named, publicly available dataset used for training in the conventional sense. |
| Dataset Splits | No | The paper describes running experiments for "100 different combinations of n and k" and checking for convergence, but it does not specify standard dataset splits (e.g., 80/10/10 split, k-fold cross-validation) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions an "ADMM implementation" but does not list any specific software dependencies or library versions needed to replicate the experiments. |
| Experiment Setup | Yes | To obtain the diagram on the left panel, for each fixed value of m, we run the algorithm for 100 different combinations of n and k, each time using a different set of Gaussian matrices B and C. If the algorithm converges to a sufficiently close neighborhood of the ground-truth solution (a distance less than 1% of the solution s ℓ2 norm), we label the experiment as successful. |