Research map / Optimization / Nonconvex and smooth optimization
The Mechanics of n-Player Differentiable Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SGA instead of simultaneous gradient descent | two-player zero-sum bimatrix game · zero-sum bimatrix game | learning-rate robustness: 0.3, 1.2 | Helped |
| Use SGA instead of simultaneous gradient descent | weak-attractor game with strong rotational force · weak-attractor game | convergence speed and robustness: 0.01, 0.032, 0.1 | Helped |
| Use SGA instead of simultaneous gradient descent | GAN with 16-Gaussian mixture data · 16-Gaussian mixture | mode collapse: 2000, 4000, 6000, 8000 | Helped |
| Add alignment to SGA (ablation) | SGA on GAN with 16-Gaussian mixture data · 16-Gaussian mixture | convergence speed | Helped |
| Add alignment to consensus optimization (ablation) | consensus optimization · GAN experiments | performance near unstable equilibria | Helped |
| Add the original game-gradient term to the consensus adjustment (ablation) | simplified consensus adjustment that drops the first term · GAN experiments | performance | Helped |
Closest papers by meaning
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- Solving Neural Min-Max Games: The Role of Architecture, Initialization & Dynamics (NeurIPS 2025 · similarity 0.88)
- Generative Adversarial Equilibrium Solvers (ICLR 2024 · similarity 0.88)
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