Beppo-Levi Theorem: Difference between revisions

From Optimal Transport Wiki
Jump to navigation Jump to search
No edit summary
No edit summary
 
(One intermediate revision by the same user not shown)
Line 1: Line 1:
The Beppo-Levi theorem is a result in measure theory that gives us conditions wherein we may then pass the integral through an infinite series of functions. That is to say, this theorem provides conditions under which the (possibly infinite) sum of the integrals is equal to the integral of the sums.
The Beppo-Levi theorem is a result in measure theory which gives a sufficient condition for interchanging an integral with an infinite series. The setting and result is essentially a particular case of the monotone convergence theorem, though one needs to be careful that all intermediary functions in the proof remain measurable so that monotone convergence may be applied.


==Statement==
==Statement==
Line 10: Line 10:
The sequence of functions <math> \left\{\sum_{n=1}^N f_n\right\}_{n\in \mathbb{N}} </math> is monotonically nondecreasing since each <math> f_n </math> is nonnegative. By the monotone convergence theorem, we thus deduce
The sequence of functions <math> \left\{\sum_{n=1}^N f_n\right\}_{n\in \mathbb{N}} </math> is monotonically nondecreasing since each <math> f_n </math> is nonnegative. By the monotone convergence theorem, we thus deduce
<math display="block"> \lim_{N\to\infty} \int \sum_{n=1}^N f_n = \int \lim_{N\to \infty} \sum_{n=1}^N f_n =\int \sum_{n=1}^\infty f_n. </math>
<math display="block"> \lim_{N\to\infty} \int \sum_{n=1}^N f_n = \int \lim_{N\to \infty} \sum_{n=1}^N f_n =\int \sum_{n=1}^\infty f_n. </math>
==References==
1. Folland, Gerald. B; "Real Analysis: Modern Techniques and Their Applications." Wiley. 2007.

Latest revision as of 01:06, 18 December 2020

The Beppo-Levi theorem is a result in measure theory which gives a sufficient condition for interchanging an integral with an infinite series. The setting and result is essentially a particular case of the monotone convergence theorem, though one needs to be careful that all intermediary functions in the proof remain measurable so that monotone convergence may be applied.

Statement

Let be the underlying measure space and let be a sequence of measurable functions with for each . Then,

Proof

We know for any two non-negative measurable functions that

Iterating this formula inductively, we find for all that
In addition, we know that the sum of two nonnegative measurable functions is again nonnegative and measurable, and induction implies that each is again measurable and nonnegative.

The sequence of functions is monotonically nondecreasing since each is nonnegative. By the monotone convergence theorem, we thus deduce

References

1. Folland, Gerald. B; "Real Analysis: Modern Techniques and Their Applications." Wiley. 2007.