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Yegorov's Theorem 📂Measure Theory

Yegorov's Theorem

Theorem1 2

Let (X,E,μ)(X, \mathcal{E}, \mu) be a measure space, called μ(X)<\mu (X) \lt \infty. Let ff be a measurable function, and {fn}\left\{ f_{n} \right\} be a sequence of measurable functions that converge to ff almost everywhere.

fnf a.e.  f_{n} \to f \text{ a.e. }

Then, for every ϵ>0\epsilon \gt 0, there exists a EXE \subset X that satisfies the following

μ(E)<ϵ and fnf on Ec \mu (E) \lt \epsilon \quad \text{ and } \quad f_{n} \rightrightarrows f \text{ on } E^{c}

Explanation

This theorem, in a nutshell, tells us that for measurable functions, pointwise convergence and uniform convergence are almost the same.

The convergence described in the theorem is also called almost uniform convergence. If the following holds, it is said that fnf_{n} converges to ff almost uniformly.

ϵ>0,EE such that μ(E)<ϵ and fnf on Ec \forall \epsilon \gt 0,\quad \exists E \subset \mathcal{E} \text{ such that } \mu (E) \lt \epsilon \text{ and } f_{n} \rightrightarrows f \text{ on } E^{c}

The following facts hold. If it converges almost uniformly,

  • it converges almost everywhere.
  • it converges in measure.

Proof

Without loss of generality, let’s assume fnff_{n} \to f for all xXx \in X. For k,nNk, n \in \N, let’s set En(k)E_{n}(k) as follows.

En(k)=m=n{x:fm(x)f(x)1k}(1) E_{n}(k) = \bigcup \limits_{m = n}^{\infty} \left\{ x : \left| f_{m}(x) - f(x) \right| \ge \frac{1}{k} \right\} \tag{1}

Then, for a fixed kk satisfying En+1(k)En(k)E_{n+1}(k) \subset E_{n}(k) and since fnff_{n} \to f, the following holds.

n=1En(k)= \bigcap\limits_{n=1}^{\infty} E_{n}(k) = \varnothing

Since it is assumed that μ(X)<\mu (X) \lt \infty, μ(En(k))0 as n\mu (E_{n}(k)) \to 0 \text{ as } n \to \infty follows. Therefore, for the given ϵ>0\epsilon \gt 0 and kNk \in \N, select a sufficiently large nkn_{k} such that μ(Enk(k))<ϵ2k\mu \left( E_{n_{k}}(k) \right) \lt \epsilon 2^{-k}. Let’s set it as E=k=1Enk(k)E = \cup_{k=1}^{\infty} E_{n_{k}}(k). Then μ(E)<ϵ\mu (E) \lt \epsilon.

Also, if xEx \notin E, for all kk, xEnk(k)x \notin E_{n_{k}}(k), and by definition (1)(1), the following holds.

fn(x)f(x)<1k for n>nk \left| f_{n}(x) - f(x) \right| \lt \frac{1}{k} \text{ for } n \gt n_{k}

Therefore, fnf on Ecf_{n} \rightrightarrows f \text{ on } E^{c}.


  1. Gerald B. Folland, Real Analysis: Modern Techniques and Their Applications (2nd Edition, 1999), p62 ↩︎

  2. Robert G. Bartle, The Elements of Integration and Lebesgue Measure (1995), p74 ↩︎