Math 2.0

Calculus of variations and optimal control; optimization Definition

Young measure

Builds on Probability measureMeasurable function

Definition

Let n,m≥1n, m \ge 1, and let Ω⊆ℝn\Omega \subseteq \R^n be a Lebesgue measurable set. Let C0(ℝm)C_0(\R^m) be the space of continuous functions φ ⁣:ℝm→ℝ\varphi \colon \R^m \to \R that tend to 00 at infinity. A Young measure on Ω\Omega with values in ℝm\R^m is a family (νx)x∈Ω(\nu_x)_{x \in \Omega} of Borel probability measures on ℝm\R^m. It must satisfy one condition: for every φ∈C0(ℝm)\varphi \in C_0(\R^m) the function x↦∫ℝmφ dνxx \mapsto \int_{\R^m} \varphi \, d\nu_x is Lebesgue measurable on Ω\Omega.

Remarks

A sequence of measurable uk ⁣:Ω→ℝmu_k \colon \Omega \to \R^m generates (νx)(\nu_x) if φ(uk)→∫φ dνx\varphi(u_k) \to \int \varphi \, d\nu_x weakly-∗* in L∞(Ω)L^\infty(\Omega) for every φ∈C0(ℝm)\varphi \in C_0(\R^m). The fundamental theorem on Young measures says that every sequence bounded in L∞(Ω;ℝm)L^\infty(\Omega; \R^m) has a subsequence that generates a Young measure. Example: a measurable u ⁣:Ω→ℝmu \colon \Omega \to \R^m gives the Young measure νx=δu(x)\nu_x = \delta_{u(x)}, since ∫φ dνx=φ(u(x))\int \varphi \, d\nu_x = \varphi(u(x)) is measurable. Example: uk(x)=sgn⁡(sin⁡(2πkx))u_k(x) = \sgn(\sin(2 \pi k x)) on (0,1)(0,1) generates the constant family νx=12(δ−1+δ1)\nu_x = \frac12 (\delta_{-1} + \delta_1). Non-example: if g ⁣:Ω→ℝmg \colon \Omega \to \R^m is not measurable, then νx=δg(x)\nu_x = \delta_{g(x)} is not a Young measure. Some open UU has a non-measurable preimage, and 1U1_U is an increasing limit of functions φj∈C0(ℝm)\varphi_j \in C_0(\R^m), so some φj∘g\varphi_j \circ g is not measurable.

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  1. Measurable function concept
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Further reading

  1. Web https://en.wikipedia.org/wiki/Young_measure
About this page
TagJC0SZT6453
URIm2://JC0SZT6453
Kinddefinition
WritingW0 stub
MSC49J45, 28A33
DefinesYoung measure
UsesProbability measure, Measurable function
Changed2 November 2026

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