Cubic Goldreich-Levin
Dain Kim, Anqi Li, Jonathan Tidor
摘要
In this paper, we give a cubic Goldreich-Levin algorithm which makes polynomially-many queries to a function f : 𝔽 n p → ℂ and produces a decomposition of f as a sum of cubic phases and a small error term. This is a natural higher-order generalization of the classical Goldreich-Levin algorithm. The classical (linear) Goldreich-Levin algorithm has wide-ranging applications in learning theory, coding theory and the construction of pseudorandom generators in cryptography, as well as being closely related to Fourier analysis. Higher-order Goldreich-Levin algorithms on the other hand involve central problems in higher-order Fourier analysis, namely the inverse theory of the Gowers U k norms, which are well-studied in additive combinatorics. The only known result in this direction prior to this work is the quadratic Goldreich-Levin theorem, proved by Tulsiani and Wolf in 2011. The main step of their result involves an algorithmic version of the U 3 inverse theorem. More complications appear in the inverse theory of the U 4 and higher norms. Our cubic Goldreich-Levin algorithm is based on algorithmizing recent work by Gowers and Milicevic who proved new quantitative bounds for the U 4 inverse theorem. Our cubic Goldreich-Levin algorithm is constructed from two main tools: an algorithmic U 4 inverse theorem and an arithmetic decomposition result in the style of the Frieze-Kannan graph regularity lemma. As one application of our main theorem we solve the problem of self-correction for cubic Reed-Muller codes beyond the list decoding radius. Additionally we give a purely combinatorial result: an improvement of the quantitative bounds on the U 4 inverse theorem.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper1
相关 Paper
- A near-optimal quadratic Goldreich-Levin algorithm (extended abstract)Jop Briët, Davi Castro-SilvaSODA 2026 · 被引用 1 次
- A High Dimensional Goldreich-Levin TheoremParker Newton, Silas Richelson, Chase WilsonSTOC 2023
- Learning Stabilizer Structure of Quantum StatesSrinivasan Arunachalam, Arkopal DuttSTOC 2026 · 被引用 5 次
- On the Power of Relaxed Local Decoding AlgorithmsTom Gur, Oded LachishSODA 2020 · 被引用 6 次
- Low Degree Local Correction Over the Boolean CubePrashanth Amireddy, Amik Raj Behera, Manaswi Paraashar, Srikanth Srinivasan 等SODA 2025 · 被引用 1 次
