Research map / Optimization / Nonconvex and smooth optimization
Accelerated Single-Call Methods for Constrained Min-Max Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Extend Optimistic Gradient to inclusion problems with one operator call and one resolvent call per iteration | Optimistic Gradient (OG) · Weak MVI inclusion problems | best-iterate tangent residual squared convergence rate: O(\frac{1}{T}) | Helped |
| Add Halpern-style anchoring to reflected gradient updates | Reflected Gradient · Negatively comonotone inclusion problems | last-iterate tangent residual convergence rate: O(\frac{1}{T}) | Helped |
| Prove a finite last-iterate guarantee for the reflected gradient method | Reflected Gradient (RG) · Convex-concave min-max optimization | last-iterate tangent residual convergence rate: O(\frac{1}{\sqrt{T}}) | Helped |
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- Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems (NeurIPS 2026 · similarity 0.89)
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