关于具有给定渐近密度和间隙密度集的Hausdorff维

Ladislav Misík, J. Šustek, B. Volkmann
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引用次数: 0

摘要

对于正整数集a a1 < a2 <···,设d(a), d¯(a) $\overline d (A)$表示其下、上渐近密度。定义间隙密度为λ(A)=lim (supn→∞)an+1an $\lambda (A) = \lim \;{\rm sup} _{n \to \infty } {{a_{n + 1} } \over {a_n }}$。本文研究了给定α, β, γ≤0≤α≤β≤1≤γ和αγ≤β时,所有集合A具有d(A) = α, d¯(A)=β $\overline d (A) = \beta $和λ(A) = γ的𝒢(α, β, γ)类。利用经典二进映射ϱ(A)=∑n=1∞χA(n)2n $\varrho (A) = \sum\nolimits_{n = 1}^\infty {{{\chi _A (n)} \over {2^n }}} $,其中χA是A的特征函数,本文的主要结果表明ϱ-image集ϱ𝒢(α,β,γ)具有Hausdorff维数dimϱ𝒢(α,β,γ)=min (α),δ{(β),1γmax (σ∈[αγ,β]δ(σ)}, $$\dim \varrho \cal {G}(\alpha ,\beta ,\gamma ) = \min \left\{ {\delta (\alpha ),\delta (\beta ), { 1 \over \gamma }\mathop {\max }\limits_{\sigma \in [\alpha \gamma ,\beta ]} \delta (\sigma )} \right\},$$其中δ是熵函数δ(x)= - x log2 x - (1 - x) log2 (1 - x)。 $$\delta (x) = - x\log _2 x - (1 - x)\;\log _2 (1 - x).$$
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Hausdorff Dimensions Related to Sets with Given Asymptotic and Gap Densities
Abstract For a set A of positive integers a1 < a2 < · · ·, let d(A), d¯(A) $\overline d (A)$ denote its lower and upper asymptotic densities. The gap density is defined as λ(A)=lim⁡ supn→∞an+1an $\lambda (A) = \lim \;{\rm sup} _{n \to \infty } {{a_{n + 1} } \over {a_n }}$ . The paper investigates the class 𝒢(α, β, γ) of all sets A with d(A) = α, d¯(A)=β $\overline d (A) = \beta $ and λ(A) = γ for given α, β, γ with 0 ≤ α ≤ β ≤ 1 ≤ γ and αγ ≤ β. Using the classical dyadic mapping ϱ(A)=∑n=1∞χA(n)2n $\varrho (A) = \sum\nolimits_{n = 1}^\infty {{{\chi _A (n)} \over {2^n }}} $ , where χA is the characteristic function of A, the main result of the paper states that the ϱ-image set ϱ𝒢(α, β, γ) has the Hausdorff dimension dimϱ𝒢(α,β,γ)=min⁡{δ(α),δ(β),1γmax⁡σ∈[αγ,β]δ(σ)}, $$\dim \varrho \cal {G}(\alpha ,\beta ,\gamma ) = \min \left\{ {\delta (\alpha ),\delta (\beta ), { 1 \over \gamma }\mathop {\max }\limits_{\sigma \in [\alpha \gamma ,\beta ]} \delta (\sigma )} \right\},$$ where δ is the entropy function δ(x)=−x log2 x−(1−x) log2 (1−x). $$\delta (x) = - x\log _2 x - (1 - x)\;\log _2 (1 - x).$$
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