Continuous-time Analysis of Anchor Acceleration
Authors: Jaewook Suh, Jisun Park, Ernest Ryu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present an adaptive method inspired by the continuous-time analyses and establish its effectiveness through theoretical analyses and experiments. We now show an experiment with the method of Theorem 7.2 applied to a decentralized compressed sensing problem Shi et al. [63]. We show the results in Figure 2. |
| Researcher Affiliation | Academia | Jaewook J. Suh Seoul National University jacksuhkr@snu.ac.kr Jisun Park Seoul National University colleenp0515@snu.ac.kr Ernest K. Ryu Seoul National University ernestryu@snu.ac.kr |
| Pseudocode | No | The paper describes algorithms and methods using mathematical notation and text, but it does not contain a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the methodology or provide a link to a source-code repository. |
| Open Datasets | Yes | We solve a compressed sensing problem of Shi et al. [63]. |
| Dataset Splits | No | The paper uses a compressed sensing problem but does not specify dataset splits (e.g., percentages, sample counts, or explicit predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions applying PG-EXTRA but does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | We choose the dimension of signal d = 100, the number of agents n = 20, the number of measurement for each agent mi = 4, ℓ1-regularization parameter ρ = 0.01, and algorithm parameter α = 0.01. |