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A finite-measure measurable set in is approximable in measure by a finite union of boxes
Statement
Assume the Axiom of Countable Choice.
Let be Lebesgue measurable with . For every there is a finite union of boxes such that
Facts & Assumptions
Given: The Axiom of Countable Choice, a natural number , a Lebesgue measurable set with finite measure, and .
Outer regularity gives an open set with (Assuming countable choice, the Lebesgue outer measure of an arbitrary subset of is the infimum of the measures of the open sets containing it).
Every open subset of is a countable disjoint union of dyadic cubes (Every open subset of is the union of a countable pairwise disjoint family of dyadic cubes).
Continuity from below applies to increasing unions of measurable sets (Continuity from below for measures).
Measure is monotone and when and (Measures are monotone, Measure of a set difference when the smaller set has finite measure).
Proof
By [L1], choose an open set with [L1, L4, given, choose] . Because , monotonicity gives
Write as a countable pairwise disjoint union [L2, L3, L4, choose] of dyadic cubes by [L2], and set . Then , so [L3] gives . Hence for some ,
Put . Since and , [step 1.1, step 1.2, L4, algebra] so The set is a finite union of boxes because each dyadic cube is a box.
Depends on
- Assuming countable choice, the Lebesgue outer measure of an arbitrary subset of $\mathbb{R}^n$ is the infimum of the measures of the open sets containing it
- Every open subset of $\mathbb{R}^n$ is the union of a countable pairwise disjoint family of dyadic cubes
- Continuity from below for measures
- Measures are monotone
- Measure of a set difference when the smaller set has finite measure
Used by
Dependency tree · two levels
41 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Terence Tao, An Introduction to Measure Theory (standard reference, not scraped)
- Walter Rudin, Real and Complex Analysis, 3rd ed. (standard reference, not scraped)