The paper introduces DGTA and DSGTA, decentralized gradient tracking algorithms for nonconvex strongly concave min-max problems with per-agent y variables, and proves O(κ²/ε²) iteration and O(κ³/ε⁴) sample complexity, though the headline rates omit spectral-gap factors.
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Decentralized Min-Max Optimization with Gradient Tracking
The paper introduces DGTA and DSGTA, decentralized gradient tracking algorithms for nonconvex strongly concave min-max problems with per-agent y variables, and proves O(κ²/ε²) iteration and O(κ³/ε⁴) sample complexity, though the headline rates omit spectral-gap factors.