Exact Optimal Accelerated Complexity for Fixed-Point Iterations
Authors: Jisun Park, Ernest K Ryu
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide experiments with CT imaging, optimal transport, and decentralized optimization to demonstrate the practical effectiveness of the acceleration mechanism. |
| Researcher Affiliation | Academia | 1Department of Mathematical Sciences, Seoul National University. |
| Pseudocode | No | The paper describes algorithms using mathematical equations and definitions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using a "Modified Shepp-Logan phantom image" and defines probability distributions/sparse signals within the text or figures, but it does not provide concrete access information (e.g., links, DOIs, formal citations) for a publicly available dataset. |
| Dataset Splits | No | The paper does not describe specific train/validation/test dataset splits. The experiments are demonstrations or simulations rather than typical machine learning experiments with data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In Section 6.2 (CT imaging), parameters are given as "α = 0.01, β = 0.03 and λ = 1.0". In Section 6.3 (Earth mover's distance), parameters are "µ = 1.0 × 10−6 and ε = 1.0". In Section 6.4 (Decentralized optimization), "stepsize α = 0.005 and regularization parameter λ = 0.002 for 100 iterations" are specified. |