Maximal Kolmogorov Complexity in a Hamming Ball

📅 2026-09-10
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🤖 AI Summary
研究了在给定Hamming距离r内字符串的最大Kolmogorov复杂度g_x(r)的可能取值及函数形式,通过界定g_x(r)的上下限,并探讨了其性质。
📝 Abstract
The minimal Kolmogorov complexity of a string within Hamming distance r of a given string x is the algorithmic rate-distortion function of x, and Vereshchagin and Vitanyi characterized completely which shapes it can have. This paper is about the opposite extreme. For a binary string x of length n let g_x(r) denote the maximal Kolmogorov complexity of a string within Hamming distance r of x; we study which values, and more generally which functions of r, this quantity can attain. First we characterize, up to an additive error O(log n), the possible values of the triple (C(x),r,g_x(r)): writing r_k for the radius of a Hamming ball of cardinality about 2^k, a triple (k,r,l) is realizable if and only if log V(r_k + r) < l < min{n, k+log V(r)}, where V(a) is the cardinality of a ball of radius a. In particular, for r_k+r > n/2 both bounds collapse to n and only l = n is realizable. The two ends of this interval correspond to the two extreme ways of placing a set of complexity k in the cube: a single Hamming ball, where the lower bound comes from Harper's isoperimetric inequality, and an error-correcting code, which for the intermediate parameters we relax to a family of centers with bounded covering multiplicity, in the spirit of list decoding. Then we turn to the function r -> g_x(r) as a whole: we establish four properties that it always has, and show that the minimal and the maximal functions consistent with these properties are both attained, for every complexity level k. Which intermediate profiles are attainable remains open.
Problem

Research questions and friction points this paper is trying to address.

Kolmogorov complexity
Hamming distance
algorithmic rate-distortion function
Innovation

Methods, ideas, or system contributions that make the work stand out.

Kolmogorov complexity
Hamming distance
algorithmic rate-distortion function
isoperimetric inequality
list decoding
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Alexander Kozachinskiy
Alexander Kozachinskiy
Postdoc, CENIA Chile
Theoretical Computer Science
N
Nikolay Vereshchagin
Moscow State University, HSE University, Yandex