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)}).
Adaptive, doubly optimal no-regret learning in strongly monotone and exp-concave games with gradient feedback.Operations Research, 73(3):1675–1702
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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)}).