pth-Order Oracle Complexity for Monotone Variational Inequalities
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Statement
Monteiro and Svaiter gave a second-order method for smooth monotone variational inequalities converging at O(T^-1.5), later improved to O(T^-1.75) for the convex-concave minimax subset. Whether the conjectured complexity for general monotone variational inequalities could be improved was open. A large-step inexact Halpern iteration achieves O(T^-2), and O(T^-p) at pth order.
Context
Improves every prior result for p >= 2 and matches the classical extragradient method at p = 1.
An explicitly stated open question in the higher-order-methods literature, resting on a well-cited Monteiro-Svaiter framework but confined to optimization theory.
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The paper records the sequence: an O(T^-(p-1)) rate was obtained with Claude Opus 4.6, and on verifying it the authors conjectured a better O(T^-p) result, for which Xinliang Zhang then found a proof with GPT-5.6 Sol. The results were subsequently verified by the human authors, who also link the model's initial proof as a public ChatGPT transcript.
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