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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions
Authors: Axel Böhm
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1 we see that beyond the theoretical guarantees OGDA+ can even provide convergence where EG+ does not. [...] 5 Numerical experiments In the following we compare EG+ method from (Diakonikolas et al., 2021) with the two methods we propose OGDA+ and EG+ with adaptive step size, see Algorithm 1 and Algorithm 3 respectively. |
| Researcher Affiliation | Academia | Axel Böhm EMAIL University of Vienna, Austria |
| Pseudocode | Yes | Algorithm 1 OGDA+ [...] Algorithm 2 stochastic OGDA+ [...] Algorithm 3 EG+ with adaptive step size |
| Open Source Code | No | No explicit statement about releasing code for the methodology described in this paper or links to code repositories are provided. The OpenReview link is for peer review, not source code. |
| Open Datasets | No | The paper refers to mathematical problems or toy examples (e.g., "von Neumann s ratio game (von Neumann, 1945)", "min-max toy example with Forsaken solution was proposed in Example 5.2 of (Hsieh et al., 2021)", "min-max problem was introduced in (Pethick et al., 2022)"). These are mathematical formulations used as test cases, not publicly available datasets with specific access information. |
| Dataset Splits | No | The paper does not use traditional datasets; instead, it evaluates methods on mathematically defined min-max problems. Therefore, the concept of training/test/validation splits is not applicable, and no such information is provided. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the numerical experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks) used for the implementation or experiments. |
| Experiment Setup | Yes | For all experiments, if not specified otherwise, we used for OGDA+ and the adaptive version of EG+ the parameter γ = 1/2. For the step size choice of Algorithm 3 we use τ = 0.99. For the Curvature EG+ method of (Pethick et al., 2022) (with their notation) we use δk equal to ρ/2, where ρ is the weak Minty parameter, if it is known and less than 1/L; and 0.499 times the step size, otherwise. Furthermore we set the parameters of the linesearch to τ = 0.9 and ν = 0.99. |