Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
Authors: Tong Yang, Michael Jordan, Tatjana Chavdarova
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analyses demonstrate clear advantages of ACVI over common first-order methods.Empirically, we document two notable advantages of ACVI over popular projection-based saddlepoint methods: (i) the ACVI iterates exhibit significantly reduced rotations, as they approach the solution from the analytic center, and (ii) while projection-based methods show extensive zigzagging when hitting a constraint, ACVI avoids this, resulting in more efficient updates 5. |
| Researcher Affiliation | Academia | Tong Yang University of California, Berkeley pptmiao@berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu Tatjana Chavdarova University of California, Berkeley tatjana.chavdarova@berkeley.edu |
| Pseudocode | Yes | Algorithm 1 ACVI pseudocode. |
| Open Source Code | Yes | Link to source code: https://github.com/Chavdarova/ACVI. |
| Open Datasets | Yes | As GANs on MNIST (Lecun & Cortes, 1998) enjoy well-established metrics, we use this setup and augment it solely with linear inequalities. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms, 2017. |
| Dataset Splits | No | No explicit percentages, sample counts, or detailed methodology for training/validation/test splits are provided for reproducibility. |
| Hardware Specification | Yes | Hardware. We used the Colab platform (https://colab.research.google.com/) and Tesla P100 GPUs. The running times are reported in App. E. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'covopt.solvers.lp' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For all the 2D problems, we set the step size of GDA, EG and OGDA to 0.1, we use k = 5 and α = 0.5 for LA-GDA, we set β = 0.08, µ 1 = 10 5, δ = 0.5 and λ0 = 0 for ACVI; and run for 50 iterations. For ACVI, we set the number of outer loop iterations to T = 20. |