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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs
Authors: Thomas Pethick, Ioannis Mavrothalassitis, Volkan Cevher
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 26,209 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,990 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 test Algorithm 1 and RAPP on Pethick et al. (2022, Ex. 5) which can be parameterized by ρ and L (see Example F.1). We set γ = 0.99/L for both methods and αk = 0.001 for RAPP and compared on ρ = 0.98/L. Algorithm 1 is additionally run on multiple problem instances of varying ρ to determined the relationship with the (automatically selected) number of inner steps. The results are shown in Figure 2, where Algorithm 1 is observed to converge using substantially fewer inner iterations than the baseline. |
| Researcher Affiliation | Academia | Laboratory for Information and Inference Systems (LIONS), EPFL (EMAIL) |
| Pseudocode | Yes | Algorithm 1 An explicit hybrid proximal extragradient method |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code or a link to a repository for the methodology described. |
| Open Datasets | No | The paper evaluates its methods on "Pethick et al. (2022, Ex. 5)", which describes a parameterized mathematical operator (a synthetic problem instance) rather than an empirical dataset that would typically be downloaded or referenced via a specific link/DOI for data. |
| Dataset Splits | No | The numerical evaluation is based on a mathematical example (an operator), not an empirical dataset. Therefore, the concept of training, validation, or test splits does not apply to the described experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the numerical evaluation. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers used for implementing or evaluating the algorithms. |
| Experiment Setup | Yes | We set γ = 0.99/L for both methods and αk = 0.001 for RAPP and compared on ρ = 0.98/L. Algorithm 1 is additionally run on multiple problem instances of varying ρ to determined the relationship with the (automatically selected) number of inner steps. |