Littlewood's First Principle: Difference between revisions

From Optimal Transport Wiki
Jump to navigation Jump to search
No edit summary
No edit summary
Line 1: Line 1:
Littlewood stated the principles in his 1944 ''Lectures on the Theory of Functions'' as:
Littlewood stated the principles in his 1944 ''Lectures on the Theory of Functions'' <ref>
{{cite book
|last=Littlewood
|first=J. E.
|authorlink=John Edensor Littlewood
|title=Lectures on the Theory of Functions
|url=https://archive.org/details/in.ernet.dli.2015.89999
|year=1944
|publisher=Oxford University Press
|oclc=297140
|page=[https://archive.org/details/in.ernet.dli.2015.89999/page/n204 26]
}}
</ref>:


<blockquote> There are three principles, roughly expressible in the following terms: Every (measurable) set is nearly a finite sum of intervals; every function (of class ''L''<sup>p</sup>) is nearly continuous; every convergent sequence of functions is nearly uniformly convergent.}} </blockquote>
<blockquote> There are three principles, roughly expressible in the following terms: Every (measurable) set is nearly a finite sum of intervals; every function (of class ''L''<sup>p</sup>) is nearly continuous; every convergent sequence of functions is nearly uniformly convergent.}} </blockquote>


This article examines the first principle and attempts to divine how restrictive the constraint of (Borel) measurability truly is for a set. This will be done in the form of two important results; namely, showing how well such a set can be approximated from above by open sets and how well such a set can be approximated from below by compact sets.  
This article examines the first principle and attempts to divine how restrictive the constraint of (Borel) measurability truly is for a set. This will be done in the form of two important results; namely, showing how well such a set can be approximated from above by open sets and how well such a set can be approximated from below by compact sets.  

Revision as of 22:58, 19 December 2020

Littlewood stated the principles in his 1944 Lectures on the Theory of Functions [1]:

There are three principles, roughly expressible in the following terms: Every (measurable) set is nearly a finite sum of intervals; every function (of class Lp) is nearly continuous; every convergent sequence of functions is nearly uniformly convergent.}}

This article examines the first principle and attempts to divine how restrictive the constraint of (Borel) measurability truly is for a set. This will be done in the form of two important results; namely, showing how well such a set can be approximated from above by open sets and how well such a set can be approximated from below by compact sets.

These two results will demonstrate how convenient Borel measurable sets are to work with and how one may more easily prove results about them by using this approximation and passing to the limit by continuity argument.

Approximation by Open Sets

The main result is the following: Let be a Borel measurable set. For every there exists a compact set with such that . Note that this implies by monotonicity.

Proof: By construction of the Borel measure, there exists a sequence of intervals such that and

Taking gives the result.[2]

Approximation by Compact Sets

The main result is the following: Let </math> E </math> be a Borel measurable set. For every </math>\varepsilon > 0</math> there exists an open set </math> U </math> with </math> E\subset U </math> such that </math> m(E)>m(U)-\varepsilon </math>. Note that this implies </math> 0< m(U)-m(E)<\varepsilon </math> by monotonicity.

Proof: First suppose </math> E </math> is bounded. If </math> E </math> is also closed, then we're done; </math> E </math> is compact. Otherwise, the boundary </math> \partial{E}=\overline{E}\backslash E </math> is nonempty. Now, </math> \partial{E} </math> is a Borel measurable set, so we can approximate it from above by the previous section. That is, there exists an open set </math> U\supset \partial{E} </math> such that </math> m(U) < m(\overline{E}\backslash E) +\varepsilon = \varepsilon </math>. Now define </math> K= \overline{E} \backslash U </math>. By construction, this set is contained in </math> U </math>. Moreover, it is closed, being the intersection of two closed sets. Finally, </math> K </math> is bounded because </math> E </math> is bounded. By the Heine-Borel Theorem, </math> K </math> is compact, and </math> K </math> satisfies the constraints of the theorem because

Now assume </math> E </math> is unbounded. Let </math> E_j = E \cap [j, j+1) </math>. By the preceding argument, for each </math> \varepsilon > 0 </math> there exist compact </math> K_j </math> such that </math> K_j \subset E_j </math> and </math> m(K_j) > m(E_j)-\varepsilon / 2^k </math>. Let </math> H_n = \bigcup_{j=-n}^{n} K_j </math>. Then </math> H_n </math> is compact and </math> m(H_n) \geq m(\bigcup_{j=-n}^n E_j) + \varepsilon </math>, and since </math> \lim_{n\to\infty} m(E)=m(\bigcup_{j=-n}^n E_j) </math>, the result follows.[2]

Littlewood's First Principle

The previous two results imply the following formulation of Littlewood's First Principle: If </math> E </math> is a Borel measurable set and </math> m(E)<+\infty </math>, then for every </math> \varepsilon > 0</math> there is a set </math> A </math> that is a finite union of open intervals such that </math> m(A\delta E) < \varepsilon </math>, where </math> \delta </math> denotes symmetric difference.

Proof: Using the previous two sections find </math> K </math> and </math> U </math> such that </math> K\subset E \subset U </math> and </math> m(E)-\epsilon < m(K) </math> and </math> m(E)+\epsilon > m(U) </math>. As noted in the proof of the first section, </math> U </math> is really a countable union of open intervals. These intervals form an open cover for </math> K </math> and since </math> K </math> is compact, only finitely many cover </math> K </math>. Defining </math> A </math> to be the union of these finitely many intervals, the result follows, since </math> m(A/E) \leq m(U/E) </math> and </math> m(E/A) \leq m(E/K) </math>.

References

  1. Template:Cite book
  2. 2.0 2.1 Gerald B. Folland, Real Analysis: Modern Techniques and Their Applications, second edition, §2.3