Math 2.0

Measure and integration Definition

Self-similar measure

Builds on Probability measurePushforward measureContraction mapping

Definition

Let n≥1n \ge 1 and N≥2N \ge 2. For i=1,…,Ni = 1, \dots, N let Si ⁣:ℝn→ℝnS_i \colon \R^n \to \R^n be a similarity with ratio ri∈(0,1)r_i \in (0, 1), that is, ∣Si(x)−Si(y)∣=ri∣x−y∣\abs{S_i(x) - S_i(y)} = r_i \abs{x - y} for all x,yx, y. Let p1,…,pN>0p_1, \dots, p_N > 0 with p1+⋯+pN=1p_1 + \cdots + p_N = 1. A Borel probability measure μ\mu on ℝn\R^n is self-similar for (Si,pi)(S_i, p_i) if μ=∑i=1Npi (Si)∗μ,\mu = \sum_{i=1}^{N} p_i \, (S_i)_* \mu, where (Si)∗μ=μ∘Si−1(S_i)_* \mu = \mu \circ S_i^{-1} is the pushforward measure.

Remarks

Each SiS_i is a contraction. Hutchinson's theorem says that there is exactly one compactly supported such μ\mu. It is the fixed point of a contraction on a space of measures, and its support is the attractor of (Si)(S_i). Example: on ℝ\R let S1(x)=x/2S_1(x) = x/2, S2(x)=x/2+1/2S_2(x) = x/2 + 1/2 and p1=p2=1/2p_1 = p_2 = 1/2, and let λ\lambda be Lebesgue measure on [0,1][0,1]. The measure (S1)∗λ(S_1)_* \lambda is uniform on [0,1/2][0, 1/2] with density 22, and (S2)∗λ(S_2)_* \lambda is uniform on [1/2,1][1/2, 1] with density 22. Their average has density 11 on [0,1][0,1], so it equals λ\lambda, and λ\lambda is self-similar. Non-example: with the same maps and p1=1/3p_1 = 1/3, p2=2/3p_2 = 2/3, the average has density 2/32/3 on [0,1/2][0, 1/2], so λ\lambda is not self-similar for these weights. The Cantor measure is self-similar for S1(x)=x/3S_1(x) = x/3, S2(x)=x/3+2/3S_2(x) = x/3 + 2/3 and p1=p2=1/2p_1 = p_2 = 1/2.

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  1. Contraction mapping concept
  2. Probability measure concept
  3. Pushforward measure concept
  4. Self-similar measure definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Self-similarity
About this page
Tag20NQDPWS3J
URIm2://20NQDPWS3J
Kinddefinition
WritingW0 stub
MSC28A80
DefinesSelf-similar measure
UsesProbability measure, Pushforward measure, Contraction mapping
Changed2 November 2026

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