A Unified Convergence Theorem for Stochastic Optimization Methods
Authors: Xiao Li, Andre Milzarek
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods. |
| Researcher Affiliation | Academia | Xiao Li School of Data Science (SDS) Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) The Chinese University of Hong Kong, Shenzhen Shenzhen, China lixiao@cuhk.edu.cn Andre Milzarek School of Data Science (SDS) Shenzhen Research Institute of Big Data (SRIBD) The Chinese University of Hong Kong, Shenzhen Shenzhen, China andremilzarek@cuhk.edu.cn |
| Pseudocode | No | The paper presents algorithmic update equations (e.g., xk+1 = prox k'(xk kgk)) but does not include structured pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | No | Under '3. If you ran experiments...', the question 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?' is answered with '[N/A]', indicating no code is provided. |
| Open Datasets | No | The paper focuses on theoretical convergence analysis and does not involve the use of datasets for training or any other purpose. |
| Dataset Splits | No | The paper focuses on theoretical convergence analysis and does not involve the use of datasets or their splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or hyperparameter details. |