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 [1].
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
Authors: Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael Jordan
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In numerical experiments, we show that this technique can converge much faster than its exact counterpart. Furthermore, for the cases when the inequality constraints are simple, we introduce an alternative variant of ACVI and establish its convergence under the same conditions. Finally, we relax the smoothness assumptions in Yang et al., yielding, to our knowledge, the first convergence result for VIs with general constraints that does not rely on the assumption that the operator is L-Lipschitz. |
| Researcher Affiliation | Academia | Tatjana Chavdarova University of California, Berkeley EMAIL Tong Yang Carnegie Mellon University EMAIL Matteo Pagliardini University of California, Berkeley & EPFL EMAIL Michael I. Jordan University of California, Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Inexact ACVI (I-ACVI) pseudocode. Algorithm 2 P-ACVI: ACVI with simple inequalities. Algorithm 3 (exact) ACVI pseudocode (Yang et al., 2023). Algorithm 4 Greedy projection method for the baseline. |
| Open Source Code | Yes | Source code: https://github.com/Chavdarova/I-ACVI. |
| Open Datasets | Yes | MNIST. We train GANs on the MNIST (Lecun & Cortes, 1998) dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits) for reproducibility. |
| Hardware Specification | Yes | We used the Colab platform (https://colab.research.google.com/) and Nvidia T4 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch library (Paszke et al., 2017)' but does not provide a specific version number for PyTorch or any other software dependency used. |
| Experiment Setup | Yes | For I-ACVI, we set ฮฒ = 0.5, ยต 1 = 10 6, ฮด = 0.8, ฮป0 = 0, K = 10, โ= 10 and the step size is 0.05. For PI-ACVI, we set ฮฒ = 0.5, and K = 5000, we use โ+ = 20 and โ0 {100, 500}. |