A novel decoupled method for distributed saddle problems achieves optimal communication complexity via multi-stage residual norm minimization, with a matching lower bound and extension to variational inequalities.
High-order reduced-gradient methods for composite variational inequalities
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Improved upper bound of Õ(ε^{-4/(3p+1)}) p-th order oracle complexity for convex-concave minimax problems via Monteiro-Svaiter acceleration, with matching lower bound Ω(ε^{-2/(3p-1)}).
citing papers explorer
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Efficient Gradient Methods for Distributed Saddle Problems
A novel decoupled method for distributed saddle problems achieves optimal communication complexity via multi-stage residual norm minimization, with a matching lower bound and extension to variational inequalities.
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Solving Convex-Concave Problems with $\tilde{\mathcal{O}}(\epsilon^{-4/(3p+1)})$ $p$th-Order Oracle Complexity
Improved upper bound of Õ(ε^{-4/(3p+1)}) p-th order oracle complexity for convex-concave minimax problems via Monteiro-Svaiter acceleration, with matching lower bound Ω(ε^{-2/(3p-1)}).