Is a measure space necessary for integration?

In summary, this abstract notion of 'integral' can be defined without requiring a measure space, and it has the nice properties of integration.
  • #1
NateTG
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Integrals are typically associated with measure spaces. For example, the Lebesgue measure for the Lebesgue integral and the Jordan measure for the Rieman integral. But it seems like it should be possible to define an analogue of integration on something weaker than a measure space. So, what is the motiviation for having integration on a measure, rather than some other method for assigning values to subsets?
 
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  • #2
And what properties would this 'assignment of values' have? I suspect it might necessarily be a measure for it to have the nice properties of integration that you want.
 
  • #3
matt grime said:
And what properties would this 'assignment of values' have? I suspect it might necessarily be a measure for it to have the nice properties of integration that you want.
I was originally thinking of something like:
If [itex]A[/itex] and [itex]B[/itex] are valued sets then
If [itex]A \subset B[/itex] then [itex]A[/itex]'s complement in [itex]B[/itex] is also valued.
If [itex]A \cap B = \null[/itex] then [itex]V(A)+V(B)=V(A \union B)[/itex].
And, [itex]V(A)\geq 0[/itex].
This is not necessarily a measure since it is not necessarily an algebra.
But it's not that difficult to hack up an abstract notion of 'integral' that doesn't even require that:
Let's say I have some set [itex]X[/itex], with [itex]M \subset P(X)[/itex] and some
function [itex]V:M \rightarrow R[/itex], where [tex]R[/tex] is a linearly ordered complete ring.
Then let [itex]\mathbb{P}[/itex] be the set of all finite partitions of [itex]X[/itex] that are subsets of [itex]M[/itex] and that do not contain the empty set.
Now, let [itex]P \in \mathbb{P}[/itex] be some partition of [itex]X[/itex] that is a subset of [itex]M[/itex]. Then (for lack of a better term) let the top of [itex]P[/tex] be
[tex]\rm{top}(P)=\sum_{p \in P} V( p) \times \rm{max}(f(p))[/tex]
where [itex] \rm{max}(f(p))[/itex] is the supremum of the image of [itex]p[/itex] in [itex]f[/itex].
And, let the bottom of [itex]P[/itex] be
[tex]\rm{bottom}(P)=\sum_{p \in P} V(p) \times \rm{min}(f(p))[/tex]
where [itex] \rm{min}(f(p))[/itex] is the infimum of the image of [itex]p[/itex] in [itex]f[/itex].
Then if [tex]\rm{sup}\{p \in \mathbb{P}, \rm{bottom}(P)\}=\rm{inf}\{p \in \mathbb{P}, \rm{top}(P)\}[/tex], let's say that [itex]\rm{sup}\{p \in \mathbb{P}, \rm{bottom}(P)\}[/itex] is the 'integral' of [itex]f[/itex] on [itex]X[/itex] and otherwise that [itex]f[/itex] is not 'integrable' on [itex]X[/itex].
 
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FAQ: Is a measure space necessary for integration?

What is a measure space?

A measure space is a mathematical concept that consists of a set and a measure defined on that set. The set represents the collection of objects being measured, while the measure assigns a numerical value to each subset of the set.

Why is a measure space necessary for integration?

A measure space is necessary for integration because integration is a way of measuring the area under a curve or the volume under a surface. Without a measure space, the concept of area or volume is not well-defined. The measure space provides the structure and framework for defining integration in a rigorous and consistent manner.

Can integration be done without a measure space?

No, integration cannot be done without a measure space. As mentioned before, a measure space is necessary for defining integration. Without a measure space, the concept of integration is undefined and cannot be applied to any mathematical problem.

Are there different types of measure spaces?

Yes, there are different types of measure spaces, such as finite measure spaces, countably infinite measure spaces, and uncountably infinite measure spaces. Each type of measure space has its own properties and is used for different purposes in mathematics.

How does a measure space relate to other mathematical concepts?

A measure space is closely related to other mathematical concepts such as probability, measure theory, and functional analysis. It provides the foundation for these concepts and enables the development of more advanced mathematical theories and applications.

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