Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exact Optimal Accelerated Complexity for Fixed-Point Iterations
Authors: Jisun Park, Ernest K Ryu
ICML 2022 | Venue PDF | 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. |