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 [1].
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
Authors: Eduard Gorbunov, Adrien Taylor, Gauthier Gidel
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By solving (10) numerically, we empirically observe that e GPEG(γ, L, N) = O(1/N) for different choices of γ, see Fig. 1a. ... Codes for verifying the potentials and convergence rates are publicly available: https://github.com/eduardgorbunov/potentials_ and_last_iter_convergence_for_VIPs, the codes rely on the PEP packages [Taylor et al., 2017b, Goujaud et al., 2022] as well as on YALMIP [Lofberg, 2004]. |
| Researcher Affiliation | Academia | Eduard Gorbunov MIPT, Russia Mila & Ude M, Canada MBZUAI, UAE EMAIL; Adrien Taylor INRIA & École Normale Supérieure, CNRS & PSL Research University, France EMAIL; Gauthier Gidel Mila & Ude M, Canada Canada CIFAR AI Chair EMAIL |
| Pseudocode | No | The paper describes the methods using mathematical recursions and equations (e.g., Proj-EG, Proj-PEG, OG) but does not provide a formally labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Codes for verifying the potentials and convergence rates are publicly available: https://github.com/eduardgorbunov/potentials_ and_last_iter_convergence_for_VIPs |
| Open Datasets | No | The paper focuses on theoretical analysis and numerical verification using performance estimation problems, not on traditional machine learning experiments that involve training on a specific dataset. Therefore, no public dataset is referenced for training. |
| Dataset Splits | No | The paper focuses on theoretical analysis and numerical verification using performance estimation problems, not on traditional machine learning experiments that involve training/validation splits. |
| Hardware Specification | No | The paper mentions using SDP solvers and YALMIP for numerical experiments but does not specify any hardware details like CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions using 'the PEP packages' and 'YALMIP' but does not specify their version numbers, which is necessary for reproducibility. |
| Experiment Setup | Yes | Under Assumption 1, for all N 0 and γ = 1/3L, we have F(x N) 2... Under Assumption 1, for all N 2 the iterates of Proj-PEG with γ = 1/4L satisfy... |