A Fast and Provable Algorithm for Sparse Phase Retrieval
Authors: Jian-Feng CAI, Yu Long, Ruixue WEN, Jiaxi Ying
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that our algorithm achieves a significantly faster convergence rate than state-of-the-art methods. ... In this section, we present a series of numerical experiments designed to validate the efficiency and accuracy of our proposed algorithm. |
| Researcher Affiliation | Academia | Jian-Feng Cai1,2, Yu Long3 , Ruixue Wen1, Jiaxi Ying1,2 1 Hong Kong University of Science and Technology 2 HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute 3 Guangxi University |
| Pseudocode | Yes | Algorithm 1 Proposed algorithm |
| Open Source Code | Yes | Our codes are available at https://github.com/jxying/Sparse PR. |
| Open Datasets | No | The paper generates synthetic data for experiments, stating: "The true signal x has s nonzero entries, where the support is selected uniformly from all subsets of [n] with cardinality s, and their values are independently generated from the standard Gaussian distribution N(0, 1)." It does not use or provide access to any publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits. It describes generating synthetic data and averaging results over multiple independent trial runs (e.g., "average of 100 independent trial runs") but does not specify data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | All experiments were conducted on a 2 GHz Intel Core i5 processor with 16 GB of RAM, and all compared methods were implemented using MATLAB. |
| Software Dependencies | No | The paper states that "all compared methods were implemented using MATLAB." However, it does not specify a version number for MATLAB or any other software libraries or dependencies used, which is required for reproducibility. |
| Experiment Setup | Yes | We fine-tune the parameters and set: α = 0.7 for Th WF; γ = 0.5, µ = 1 and |I| = m/6 for SPARTA; η = 0.95 for both HTP and our algorithm. The maximum number of iterations for each algorithm is 1000. |