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 [1].
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Authors: Hamid Reza Feyzmahdavian, Mikael Johansson
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms. The results are simple to apply and give explicit estimates for how the degree of asynchrony impacts the convergence rates of the iterates. Our results shorten, streamline and strengthen existing convergence proofs for several asynchronous optimization methods and allow us to establish convergence guarantees for popular algorithms that were thus far lacking a complete theoretical understanding. |
| Researcher Affiliation | Collaboration | Hamid Reza Feyzmahdavian EMAIL ABB Corporate Research V aster as, Sweden Mikael Johansson EMAIL Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm, Sweden |
| Pseudocode | Yes | Algorithm 1 Proximal Gradient (Pg) Method |
| Open Source Code | No | The paper does not explicitly mention the release of source code for the methodology described. |
| Open Datasets | No | The paper discusses various optimization problems and algorithms, sometimes using examples like 'least-squares regression' or 'logistic regression' to illustrate applications. However, it does not conduct experiments on specific datasets nor provides concrete access information for any open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets, therefore, it does not provide information about dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithmic guarantees. It discusses general parallel computing environments like 'individual cores in a CPU' or 'servers in a geographically dispersed cluster' as motivation, but does not specify any particular hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe an experimental setup, therefore, it does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting novel convergence results and algorithmic guarantees for asynchronous iterations. It does not include an experimental setup with specific hyperparameters or training configurations. |