Research map / Optimization / Nonconvex and smooth optimization
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Smoothed-GDA instead of the algorithm of [20] | robust MNIST network · MNIST natural accuracy | accuracy (%): 98.20% -> 98.89% | Helped |
| use Smoothed-GDA instead of the algorithm of [20] | robust MNIST network · MNIST PGD40 eps=0.4 | accuracy (%): 94.22% -> 94.51% | Helped |
| use Smoothed-GDA instead of the algorithm of [20] | robust MNIST network · MNIST FGSM eps=0.4 | accuracy (%): 96.23% -> 95.81% | Mixed |
| use Smoothed-GDA instead of the algorithm of [20] | robust MNIST network · MNIST training loss | epochs to loss below 0.2: 14 -> 5 epochs | Helped |
Closest papers by meaning
- On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems (ICML 2020 · similarity 0.94)
- Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization (NeurIPS 2023 · similarity 0.92)
- Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems (NeurIPS 2020 · similarity 0.91)
- High-probability complexity bounds for stochastic non-convex minimax optimization (NeurIPS 2024 · similarity 0.90)
- Fundamental Benefit of Alternating Updates in Minimax Optimization (ICML 2024 · similarity 0.89)
- Semi-infinite Nonconvex Constrained Min-Max Optimization (NeurIPS 2025 · similarity 0.87)
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