Does Convergence in Measure Always Guarantee Almost Uniform Convergence?

In summary, the question is whether a sequence of measurable functions that converges in measure can also have a subsequence that converges almost uniformly. This is true if the measure of the domain is finite, but in general, is it still true? The definition of almost uniform convergence is also clarified, and it is not the same as convergence in L^\infty. The OP's claim is shown to be true for all measure spaces, thanks to the F. Riesz convergence lemma/theorem.
  • #1
Thorn
23
0
If a sequence of measurable functions (real-valued) converges in measure, is it true that you can find a subsequence that converges almost uniformly? (This is obviously true if m*(domain) is finite...but in general is it?) If so, can someone outline a little why?
 
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  • #2
Does the almost uniform convergence mean that the essential supremum of f-f_n approaches zero?

If so, I think I came up with a very simple counter example to your claim that m*(domain)<oo would be enough for this.
 
  • #3
jostpuur said:
Does the almost uniform convergence mean that the essential supremum of f-f_n approaches zero?
No, the usual definition goes something like this: [itex]f_n \to f[/itex] almost uniformly if for every [itex]\epsilon > 0[/itex] there is a set E of measure less than [itex]\epsilon[/itex] such that [itex]f_n \to f[/itex] uniformly on the complement of E.

This is not the same as convergence in [itex]L^\infty[/itex].

What the OP is asking turns out to be true, for all measure spaces. It follows from a result that's sometimes called the "F. Riesz convergence lemma/theorem".
 

FAQ: Does Convergence in Measure Always Guarantee Almost Uniform Convergence?

What is convergence in measure?

Convergence in measure is a concept in measure theory that describes the behavior of a sequence of measurable functions. It states that as the index of the sequence increases, these functions will eventually become increasingly similar to each other, or "converge" in some sense.

How is convergence in measure different from pointwise convergence?

Pointwise convergence is a stronger form of convergence than convergence in measure. Pointwise convergence requires that for every point in the domain, the sequence of function values converges to a single limit. However, convergence in measure only requires the sequence of functions to converge in a weaker sense, such as to a set of measure zero.

What is the relationship between convergence in measure and almost everywhere convergence?

Almost everywhere convergence is a form of convergence in which the sequence of functions converges everywhere except on a set of measure zero. Convergence in measure, on the other hand, only requires the sequence of functions to converge on a set of measure zero. Therefore, almost everywhere convergence implies convergence in measure, but the converse is not necessarily true.

Why is convergence in measure an important concept in measure theory?

Convergence in measure is important because it is a useful tool for proving theorems and studying the behavior of sequences of functions. It is also a key concept in probability theory and is often used to define important concepts such as the convergence of random variables.

What are some applications of convergence in measure?

Convergence in measure has many applications in mathematics, including in the study of integration, Fourier analysis, and probability theory. It is also used in other fields such as statistics and machine learning to analyze the behavior of sequences of data or functions.

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