Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Accelerated Single-Call Methods for Constrained Min-Max Optimization
Authors: Yang Cai, Weiqiang Zheng
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 29,287 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,291 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide illustrative numerical experiments in Appendix E. |
| Researcher Affiliation | Academia | Yang Cai Yale University EMAIL Weiqiang Zheng Yale University EMAIL |
| Pseudocode | No | The paper describes update rules for algorithms (e.g., Optimistic Gradient (OG) and Accelerated Reflected Gradient (ARG)) in text, but it does not present them in a formalized pseudocode block or algorithm environment. |
| Open Source Code | Yes | The code can be found in the Supplementary Material. |
| Open Datasets | No | The paper describes a 'Test Problem' used for numerical experiments (Problem 1 in (Malitsky, 2015)) which is a specific mathematical formulation, not a public dataset in the traditional sense of a collection of data samples for training. No explicit access information (link, DOI, citation with authors/year) is provided for a public dataset. |
| Dataset Splits | No | The paper describes a 'Test Problem' and experimental setup, but it does not mention any training, validation, or testing splits for a dataset. The evaluation is based on a convergence criterion (residual). |
| Hardware Specification | Yes | We run experiments using Python 3.9 on jupyter-notebook, on Mac Book Air (M1, 2020) running mac OS 12.5.1. |
| Software Dependencies | Yes | We run experiments using Python 3.9 on jupyter-notebook, on Mac Book Air (M1, 2020) running mac OS 12.5.1. |
| Experiment Setup | Yes | We denote η to be the step size and the termination criteria is the residual (operator norm) ||F(zt)|| ε. ... With step size η = 0.4, EG is slower than RG. ... With the optimized step size η = 0.7... With step size η = 0.5, FEG is slower than ARG. With the optimized step size η = 1... |