Quasi-Newton Methods for Saddle Point Problems
Authors: Chengchang Liu, Luo Luo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments show proposed algorithms outperform classical first-order methods. and Section 5 Numerical Experiments with subsections like AUC Maximization and Adversarial Debiasing and Figure 1 and 2 showing iteration numbers vs. g(z) 2 and CPU time (second) vs. g(z) 2. |
| Researcher Affiliation | Academia | Chengchang Liu Department of Computer Science & Engineering The Chinese University of Hong Kong 7liuchengchang@gmail.com Luo Luo School of Data Science Fudan University luoluo@fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1 Fast-Chol(H, L, u), Algorithm 2 Random-Broyden-Quadratic, Algorithm 3 Random-BFGS-Quadratic, Algorithm 4 Random-SR1-Quadratic, Algorithm 5 Random-Broyden-General, Algorithm 6 Random-BFGS-General, Algorithm 7 Random-SR1-General |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We present them in the supplemental material. |
| Open Datasets | Yes | We evaluate all algorithms on three real-world datasets a9a , w8a and sido0 . and fairness-aware binary classification dataset adult , bank market and law school [30]. |
| Dataset Splits | No | The provided text mentions using datasets but does not explicitly detail training, validation, and test splits with percentages or specific methods for partitioning the data. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix E. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries or programming languages used in the experiments. |
| Experiment Setup | Yes | We set λ = 100/n (for AUC Maximization) and We set the parameters β, λ and γ as 0.5, 10 4 and 10 4 respectively. The dimension of the problem is d = m + 1. Since the objective function is non-quadratic, we conduct the proposed algorithms in Section 3.3 (Algorithm 5, 6 and 7) here. We use extragradient as warm up to achieve the local condition for proposed algorithms. (for Adversarial Debiasing). |