Young measure

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In mathematical analysis, a Young measure is a parameterized measure that is associated with certain subsequences of a given bounded sequence of measurable functions. Young measures have applications in the calculus of variations and the study of nonlinear partial differential equations, as well as in various optimization (or optimal control problems). They are named after Laurence Chisholm Young who invented them, however, in terms of linear functionals already in 1937 still before the measure theory has been developed.


We let \{ f_k \}_{k=1}^\infty be a bounded sequence in L^\infty (U,\mathbb{R}^m), where U denotes an open bounded subset of \mathbb{R}^n. Then there exists a subsequence \{ f_{k_j} \}_{j=1}^\infty \subset \{ f_k \}_{k=1}^\infty and for almost every x \in U a Borel probability measure \nu_x on \mathbb{R}^m such that for each F \in C(\mathbb{R}^m) we have F(f_{k_j}) \overset{\ast}{\rightharpoonup} \int_{\mathbb{R}^m} F(y)d\nu_\cdot(y) in L^\infty (U). The measures \nu_x are called the Young measures generated by the sequence \{ f_k \}_{k=1}^\infty.


For every minimizing sequence u_n of I(u) = \int_0^1 (u_x^2-1)^2 + u^2dx subject to u(0)=u(1)=0 , the seuqence of derivatives u'_n generates the Young measures \nu_x= \frac{1}{2} \delta_{-1} + \frac{1}{2}\delta_1. This captures the essential features of all minimizing sequences to this problem, namely developing finer and finer slopes of \pm 1 (or close to \pm 1).


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