Multi-Frequency Phase Synchronization
Authors: Tingran Gao, Zhizhen Zhao
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section contains detailed numerical results under both additive Gaussian noise and random corruption models, for both U(1) and SO(3). In Figure 2 and Figure 3, we measure the correlation between the output and the truth phase vector for various singleand multi-frequency synchronization methods |
| Researcher Affiliation | Academia | 1Committee on Computational and Applied Mathematics, Department of Statistics, University of Chicago, Chicago IL, USA 2Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana IL, USA. |
| Pseudocode | Yes | Algorithm 1 Periodogram Peak Extraction with Spectral Methods (PPE-SPC) and Algorithm 2 Multi-Frequency Generalized Power Method (MFGPM) |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a link to a source code repository for the methodology described. |
| Open Datasets | No | Input data are generated from the random corruption model (5) with r = 0.1 and n = 100. In all experiments with Gaussian noise, we keep σk σ n/λ where λ > 0 is the signalto-noise ratio (SNR); for the random corruption model (3) we set r λ/ n. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It describes data generation and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We fix n = 100 and vary λ and kmax to evaluate and compare the performance of different algorithms. In all these experiments, the Fourier transform (27) is numerically evaluated using m = 1000 elements uniformly sampled in SO(3). |