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..
Increasing Iterate Averaging for Solving Saddle-Point Problems
Authors: Yuan Gao, Christian Kroer, Donald Goldfarb7537-7544
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments on zero-sum game solving, market equilibrium computation and image denoising demonstrate the effectiveness of the proposed schemes. |
| Researcher Affiliation | Academia | Yuan Gao, Christian Kroer, Donald Goldfarb Columbia University, Department of Industrial Engineering and Operations Research EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Nonlinear primal-dual algorithm (PDA); Algorithm 2 Relaxed primal-dual algorithm (RPDA); Algorithm 3 Inertial primal-dual algorithm (IPDA); Algorithm 4 PDAL: Primal-dual algorithm with linesearch; Algorithm 5 Mirror Descent (MD) and Mirror Prox (MP) |
| Open Source Code | No | The paper mentions an extended manuscript at https://arxiv.org/abs/1903.10646, which is a link to the paper itself, not explicitly to source code for the methodology. |
| Open Datasets | Yes | EFG benchmark instances Kuhn and Leduc poker (see, e.g., (Kroer et al. 2018)). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For all algorithms, we use their default stepsizes and Euclidean DGF. We perform T = 2000 iterations. The choices of algorithm hyperparameters are completely analogous to those in solving matrix games. [...] Following Chambolle and Pock (2011), to align it with (4), choose f = 0, g(u) = λ u g 1 with λ = 1.5 and h (p) = δP (p); in this way, the proximal mappings yield closed-form formulas. We use PDA with default, static hyperparameters used in (Chambolle and Pock 2011) and run for T = 1000 iterations. |