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Riesz–Markov–Kakutani representation theorem

Riesz–Markov–Kakutani representation theorem is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Riesz–Markov–Kakutani representation theorem rather than just read about it. In short: In mathematics, the Riesz–Markov–Kakutani representation theorem relates linear functionals on spaces of continuous functions on a locally compact space to measures in measure theory. The theorem is named for Frigyes Riesz (1909) who introduced it for continuous functions on the unit interval, Andrey Markov (1938) who extended the result to some non-compact spaces, and Shizuo Kakutani (1941) who extended the result…

Key takeaways

  • Riesz–Markov–Kakutani representation theorem belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Riesz–Markov–Kakutani representation theorem to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Riesz–Markov–Kakutani representation theorem from memory before moving on to harder problems.

Reference excerpt

In mathematics, the Riesz–Markov–Kakutani representation theorem relates linear functionals on spaces of continuous functions on a locally compact space to measures in measure theory. The theorem is named for Frigyes Riesz (1909) who introduced it for continuous functions on the unit interval, Andrey Markov (1938) who extended the result to some non-compact spaces, and Shizuo Kakutani (1941) who extended the result to compact Hausdorff spaces. There are many closely related variations of the theorem, as the linear functionals can be complex, real, or positive, the space they are defined on may be the unit interval or a compact space or a locally compact space, the continuous functions may be vanishing at infinity or have compact support, and the measures can be Baire measures or regular Borel measures or Radon measures or signed measures or complex measures.

The representation theorem for positive linear functionals on Cc(X) The statement of the theorem for positive linear functionals on Cc(X), the space of compactly supported complex-valued continuous functions, is as follows: Theorem Let X be a locally compact Hausdorff space and ψ {\displaystyle \psi } a positive linear functional on Cc(X). Then there exists a unique positive Borel measure μ {\displaystyle \mu } on X such that

ψ ( f ) = ∫ X f ( x ) d μ ( x ) , ∀ f ∈ C c ( X ) , {\displaystyle \psi (f)=\int _{X}f(x)\,d\mu (x),\quad \forall f\in C_{c}(X),}

which has the following additional properties for some Σ {\displaystyle \Sigma } containing the Borel σ-algebra on X:

μ ( K ) < ∞ {\displaystyle \mu (K)<\infty } for every compact K ⊂ X {\displaystyle K\subset X} , Outer regularity: μ ( E ) = inf { μ ( U ) : E ⊆ U , U open } {\displaystyle \mu (E)=\inf\{\mu (U):E\subseteq U,U{\mbox{ open}}\}} holds for every Borel set E ∈ Σ {\displaystyle E\in \Sigma } ; Inner regularity: μ ( E ) = sup { μ ( K ) : K ⊆ E , K compact } {\displaystyle \mu (E)=\sup\{\mu (K):K\subseteq E,K{\mbox{ compact}}\}} holds whenever E {\displaystyle E} is open or when E {\displaystyle E} is Borel and μ ( E ) < ∞ {\displaystyle \mu (E)<\infty } ;

( X , Σ , μ ) {\displaystyle (X,\Sigma ,\mu )} is a complete measure space One approach to measure theory is to start with a Radon measure, defined as a positive linear functional on Cc(X). This is the way adopted by Bourbaki; it does of course assume that X starts life as a topological space, rather than simply as a set. For locally compact spaces an integration theory is then recovered. Without the condition of regularity the Borel measure need not be unique. For example, let X be the set of ordinals at most equal to the first uncountable ordinal Ω, with the topology generated by "open intervals". The linear functional taking a continuous function to its value at Ω corresponds to the regular Borel measure with a point mass at Ω. However it also corresponds to the (non-regular) Borel measure that assigns measure 1 to any Borel set B ⊆ [ 0 , Ω ] {\displaystyle B\subseteq [0,\Omega ]} if there is closed and unbounded set C ⊆ [ 0 , Ω ] {\displaystyle C\subseteq [0,\Omega ]} with C ⊆ B {\displaystyle C\subseteq B} , and assigns measure 0 to other Borel sets. (In particular the singleton { Ω } {\displaystyle \{\Omega \}} gets measure 0, contrary to the point mass measure.)

The representation theorem for the continuous dual of C0(X) The following representation, also referred to as the Riesz–Markov theorem, gives a concrete realisation of the topological dual space of C0(X), the set of continuous functions on X which vanish at infinity. Theorem Let X be a locally compact Hausdorff space. For any continuous linear functional ψ {\displaystyle \psi } on C0(X), there is a unique complex-valued regular Borel measure μ {\displaystyle \mu } on X such that

ψ ( f ) = ∫ X f ( x ) d μ ( x ) , ∀ f ∈ C 0 ( X ) . {\displaystyle \psi (f)=\int _{X}f(x)\,d\mu (x),\quad \forall f\in C_{0}(X).}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Riesz–Markov–Kakutani representation theorem

Start with the simplest possible case. Write down what Riesz–Markov–Kakutani representation theorem claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Riesz–Markov–Kakutani representation theorem before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Riesz–Markov–Kakutani representation theorem ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Riesz–Markov–Kakutani representation theorem

In research
Riesz–Markov–Kakutani representation theorem appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Riesz–Markov–Kakutani representation theorem in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Riesz–Markov–Kakutani representation theorem is common in secondary-school and first-year university syllabi. It links to neighbouring topics Duality (mathematics), Integral representations, Linear functionals, so understanding it makes those chapters shorter.
In everyday life
Look for Riesz–Markov–Kakutani representation theorem outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Riesz–Markov–Kakutani representation theorem in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Riesz–Markov–Kakutani representation theorem means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Riesz–Markov–Kakutani representation theorem out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Riesz–Markov–Kakutani representation theorem in simple terms?

In mathematics, the Riesz–Markov–Kakutani representation theorem relates linear functionals on spaces of continuous functions on a locally compact space to measures in measure theory. The theorem is named for Frigyes Riesz (1909) who introduced it for continuous functions on the unit interval, Andr…

Why does Riesz–Markov–Kakutani representation theorem matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Riesz–Markov–Kakutani representation theorem?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Riesz–Markov–Kakutani representation theorem.

Tags

  • Duality (mathematics)
  • Integral representations
  • Linear functionals
  • Theorems in functional analysis

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