Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Authors: Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we also provide experimental results to support our theory. |
| Researcher Affiliation | Academia | Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo University of Southern California {chenyu.wei,leechung,mengxiao.zhang,haipengl}@usc.edu |
| Pseudocode | No | The paper describes the Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) algorithms using mathematical equations and iterative steps (e.g., 'xt = ΠX bxt − η∇xf(xt−1, yt−1)'), but it does not present this as a formal pseudocode block or explicitly label it as 'Algorithm'. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for experiments (e.g., 'generate a random matrix with each entry Gij drawn uniformly at random from [-1, 1]') and defines specific functional forms for f(x,y) and constraint sets (e.g., 'X = Y = {(a, b), 0 <= a, b <= 1, a + b = 1}'). However, it does not provide access information (link, DOI, citation with authors/year) for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments on synthetically generated data and does not specify any explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | We compare the performances of OGDA and OMWU. For both algorithms, we choose a series of different learning rates and compare their performances, as shown in Figure 1. |