Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs
Authors: Thomas Pethick, Ioannis Mavrothalassitis, Volkan Cevher
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |