Increasing Iterate Averaging for Solving Saddle-Point Problems
Authors: Yuan Gao, Christian Kroer, Donald Goldfarb7537-7544
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 gao.yuan@columbia.edu, christian.kroer@columbia.edu, goldfarb@columbia.edu |
| 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. |