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Jiajin Li

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Preprint Sep 2026

Optimal Deterministic First-Order Oracle Complexity for Nonconvex-Concave Minimax Optimization

We study the deterministic first-order oracle complexity of smooth nonconvex-concave minimax optimization over a bounded convex dual domain. Let $\ell$ denote the joint smoothness constant, $D_{\mathcal{Y}}$ the diameter of the dual domain, and $\Delta$ the initial gap. We prove that every deterministic first-order algorithm requires $\Omega(\ell^2D_{\mathcal{Y}}\Delta/\epsilon^3)$ oracle queries in the worst case to find an $\epsilon$-optimization-stationary point whenever $\epsilon\lesssim\min\{\ell D_{\mathcal{Y}},\sqrt{\ell\Delta}\}$. We then develop Tracked-FOAM, a first-order method that attains a matching upper bound, removing the logarithmic factor from previous upper bounds. Together, these results establish the optimal dependence on all problem parameters in the stated regime.

Si-Yu Pan, Tao-Li Zheng, Jia-Jin Li · 0 citations
Preprint Aug 2026

Lower Bounds for Nonconvex-P{\L} Minimax Optimization

It is proved that every deterministic first-order method requires $\Omega(\ell\Delta\kappa/\epsilon^2)$ oracle queries in the worst case to find $x$ satisfying $\Phi(0)-\inf_x\Phi(x)$ and that the linear dependence on $\kappa$ is unavoidable for deterministic first-order methods.

Si-Yu Pan, Jia-Jin Li · 1 citation
Preprint Aug 2026

Optimal Deterministic Oracle Complexity for Weakly Convex Optimization

It is proved that every deterministic first-order algorithm requires a first-order oracle that returns both the function value and the full subdifferential at every query point, and establishes the optimal deterministic oracle complexity.

Jia-Jin Li, Si-Yu Pan · 3 citations

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