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..
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems
Authors: Sucheol Lee, Donghwan Kim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proposes a two-time-scale EG with anchoring, named fast extragradient (FEG), that has a fast O(1/k2) rate on the squared gradient norm for smooth structured nonconvex-nonconcave problems; the corresponding saddle-gradient operator satisfies the negative comonotonicity condition. This paper further develops its backtracking line-search version, named FEG-A, for the case where the problem parameters are not available. The stochastic analysis of FEG is also provided. and Theorem 4.1. For the L-Lipschitz continuous and ρ-comonotone operator F with ρ > 1/2L and for any z Z (F ), the sequence {zk}k 0 generated by FEG satisfies, for all k 1, F zk 2 <= 4 z0 z 2 / (1/L + 2ρ)^2 k^2. |
| Researcher Affiliation | Academia | Sucheol Lee Department of Mathematical Sciences KAIST Daejeon, Republic of Korea Donghwan Kim Department of Mathematical Sciences KAIST Daejeon, Republic of Korea |
| Pseudocode | Yes | Algorithm 1 Fast extragradient (FEG) method, Algorithm 2 Fast extragradient method with adaptive step size (FEG-A), Algorithm 3 Stochastic fast extragradient (S-FEG) method |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper uses a 'Toy example' with a simple quadratic function f(x, y) = ρL^2/2 y^2 for illustration, which is a mathematical construct and not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper primarily presents theoretical analysis and a mathematical toy example, thus it does not provide specific training/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For the case ρ = 1/3L and L = 1, Figure 2 illustrates that the FEG converges with an accelerated rate... |