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原文摘要

Montanari's incremental approximate message passing (IAMP) algorithm [FOCS 2019; SIAM J. Comput., 2025] and the zero-temperature formulation of El Alaoui, Montanari and Sellke (AMS) [Ann. Probab., 2021] find a $(1-\varepsilon)$ approximation to the maximum Sherrington-Kirkpatrick (SK) energy in $C(\varepsilon)N^2$ operations for fixed accuracy, with probability tending to one as $N\to\infty$, under their respective full-support and access-model assumptions. However, it was unknown whether this approximation could be achieved with work polynomial in both $N$ and $1/\varepsilon$, together with quantitative guarantees at finite dimension. Under the same assumptions as AMS, we introduce grouped IAMP with exact Gaussian query outputs (GEG-IAMP), which is the first SK approximation algorithm with total work polynomial in $N$ and $1/\varepsilon$ and a finite-size guarantee above a dimension threshold polynomial in $1/\varepsilon$ and $\log(1/α)$. More precisely, for every $α\in(0,1/2)$, it returns a $(1-\varepsilon)$ approximation with probability at least $1-α$ whenever $N\ge \mathsf{K}\varepsilon^{-2}\log^2(e/\varepsilon)\log(\mathsf{K}/α)$, using $O(N^2/\varepsilon)$ work and $O(N^2)$ storage. GEG-IAMP replaces the Onsager correction by Reeves's coupling construction [arXiv:2508.10782] and groups Euler-Maruyama steps into $O(1/\varepsilon)$ matrix-vector products. We also prove a relative guarantee for AMS IAMP with $O(N^2\varepsilon^{-3}\log^2(e/\varepsilon))$ work but a sufficient dimension exponential in inverse accuracy. By returning a fixed configuration below its dimension threshold, GEG-IAMP also provides an asymptotic fully polynomial randomized approximation scheme (AFPRAS), with an additive error independent of dimension.