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Essential range
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In mathematics, particularly measure theory, the essential range, or the set of essential values, of a function is intuitively the 'non-negligible' range of the function: It does not change between two functions that are equal almost everywhere. One way of thinking of the essential range of a function is the set on which the range of the function is 'concentrated'.

Contents

Y = C

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Formal definition

Let ( X , A , μ μ ) {\displaystyle (X,{\cal {A}},\mu )} be a measure space, and let ( Y , T ) {\displaystyle (Y,{\cal {T}})} be a topological space. For any ( A , σ σ ( T ) ) {\displaystyle ({\cal {A}},\sigma ({\cal {T}}))} -measurable function f : X → → Y {\displaystyle f:X\to Y} , we say the essential range of f {\displaystyle f} to mean the set

e s s . i m ⁡ ⁡ ( f ) = { y ∈ ∈ Y ∣ ∣ 0 < μ μ ( f − − 1 ( U ) ) for all U ∈ ∈ T with y ∈ ∈ U } . {\displaystyle \operatorname {ess.im} (f)=\left\{y\in Y\mid 0<\mu (f^{-1}(U)){\text{ for all }}U\in {\cal {T}}{\text{ with }}y\in U\right\}.} cite-ref-1[1]cite-ref-2[2]cite-ref-3[3]

Equivalently, e s s . i m ⁡ ⁡ ( f ) = supp ⁡ ⁡ ( f ∗ ∗ μ μ ) {\displaystyle \operatorname {ess.im} (f)=\operatorname {supp} (f_{*}\mu )} , where f ∗ ∗ μ μ {\displaystyle f_{*}\mu } is the pushforward measure onto σ σ ( T ) {\displaystyle \sigma ({\cal {T}})} of μ μ {\displaystyle \mu } under f {\displaystyle f} and supp ⁡ ⁡ ( f ∗ ∗ μ μ ) {\displaystyle \operatorname {supp} (f_{*}\mu )} denotes the support of f ∗ ∗ μ μ . {\displaystyle f_{*}\mu .} cite-ref-4[4]

Essential values

The phrase "essential value of f {\displaystyle f} " is sometimes used to mean an element of the essential range of f . {\displaystyle f.} cite-ref-5[5]cite-ref-6[6]

Special cases of common interest

Y = C

Say ( Y , T ) {\displaystyle (Y,{\cal {T}})} is C {\displaystyle \mathbb {C} } equipped with its usual topology. Then the essential range of f is given by

e s s . i m ⁡ ⁡ ( f ) = { z ∈ ∈ C ∣ ∣ for all ε ε ∈ ∈ R > 0 : 0 < μ μ { x ∈ ∈ X : | f ( x ) − − z | < ε ε } } . {\displaystyle \operatorname {ess.im} (f)=\left\{z\in \mathbb {C} \mid {\text{for all}}\ \varepsilon \in \mathbb {R} _{>0}:0<\mu \{x\in X:|f(x)-z|<\varepsilon \}\right\}.} cite-ref-7[7]cite-ref-8[8]cite-ref-9[9]cite-ref-10[10]cite-ref-11[11]

In other words: The essential range of a complex-valued function is the set of all complex numbers z such that the inverse image of each ε-neighbourhood of z under f has positive measure.

( Y , T ) is discrete

Say ( Y , T ) {\displaystyle (Y,{\cal {T}})} is discrete, i.e., T = P ( Y ) {\displaystyle {\cal {T}}={\cal {P}}(Y)} is the power set of Y , {\displaystyle Y,} i.e., the discrete topology on Y . {\displaystyle Y.} Then the essential range of f is the set of values y in Y with strictly positive f ∗ ∗ μ μ {\displaystyle f_{*}\mu } -measure:

e s s . i m ⁡ ⁡ ( f ) = { y ∈ ∈ Y : 0 < μ μ ( f pre { y } ) } = { y ∈ ∈ Y : 0 < ( f ∗ ∗ μ μ ) { y } } . {\displaystyle \operatorname {ess.im} (f)=\{y\in Y:0<\mu (f^{\text{pre}}\{y\})\}=\{y\in Y:0<(f_{*}\mu )\{y\}\}.} cite-ref-12[12]cite-ref-13[13]cite-ref-14[14]

Properties

• The essential range of a measurable function, being the support of a measure, is always closed.
• The essential range ess.im(f) of a measurable function is always a subset of im ⁡ ⁡ ( f ) ¯ ¯ {\displaystyle {\overline {\operatorname {im} (f)}}} .
• The essential image cannot be used to distinguish functions that are almost everywhere equal: If f = g {\displaystyle f=g} holds μ μ {\displaystyle \mu } -almost everywhere, then e s s . i m ⁡ ⁡ ( f ) = e s s . i m ⁡ ⁡ ( g ) {\displaystyle \operatorname {ess.im} (f)=\operatorname {ess.im} (g)} .
• These two facts characterise the essential image: It is the biggest set contained in the closures of im ⁡ ⁡ ( g ) {\displaystyle \operatorname {im} (g)} for all g that are a.e. equal to f:

e s s . i m ⁡ ⁡ ( f ) = ⋂ ⋂ f = g a.e. im ⁡ ⁡ ( g ) ¯ ¯ {\displaystyle \operatorname {ess.im} (f)=\bigcap _{f=g\,{\text{a.e.}}}{\overline {\operatorname {im} (g)}}} .

• The essential range satisfies ∀ ∀ A ⊆ ⊆ X : f ( A ) ∩ ∩ e s s . i m ⁡ ⁡ ( f ) = ∅ ∅ ⟹ ⟹ μ μ ( A ) = 0 {\displaystyle \forall A\subseteq X:f(A)\cap \operatorname {ess.im} (f)=\emptyset \implies \mu (A)=0} .
• This fact characterises the essential image: It is the smallest closed subset of C {\displaystyle \mathbb {C} } with this property.
• The essential supremum of a real valued function equals the supremum of its essential image and the essential infimum equals the infimum of its essential range. Consequently, a function is essentially bounded if and only if its essential range is bounded.
• The essential range of an essentially bounded function f is equal to the spectrum σ σ ( f ) {\displaystyle \sigma (f)} where f is considered as an element of the C*-algebra L ∞ ∞ ( μ μ ) {\displaystyle L^{\infty }(\mu )} .

Examples

• If μ μ {\displaystyle \mu } is the zero measure, then the essential image of all measurable functions is empty.
• This also illustrates that even though the essential range of a function is a subset of the closure of the range of that function, equality of the two sets need not hold.
• If X ⊆ ⊆ R n {\displaystyle X\subseteq \mathbb {R} ^{n}} is open, f : X → → C {\displaystyle f:X\to \mathbb {C} } continuous and μ μ {\displaystyle \mu } the Lebesgue measure, then e s s . i m ⁡ ⁡ ( f ) = im ⁡ ⁡ ( f ) ¯ ¯ {\displaystyle \operatorname {ess.im} (f)={\overline {\operatorname {im} (f)}}} holds. This holds more generally for all Borel measures that assign non-zero measure to every non-empty open set.

Extension

The notion of essential range can be extended to the case of f : X → → Y {\displaystyle f:X\to Y} , where Y {\displaystyle Y} is a separable metric space. If X {\displaystyle X} and Y {\displaystyle Y} are differentiable manifolds of the same dimension, if f ∈ ∈ {\displaystyle f\in } VMO ( X , Y ) {\displaystyle (X,Y)} and if e s s . i m ⁡ ⁡ ( f ) ≠ ≠ Y {\displaystyle \operatorname {ess.im} (f)\neq Y} , then deg ⁡ ⁡ f = 0 {\displaystyle \deg f=0} .cite-ref-15[15]

See also
References

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cite-note-22. citerefkuksinshirikyan2012Kuksin, Sergei; Shirikyan, Armen (2012). Mathematics of Two-Dimensional Turbulence. Cambridge University Press. p. 292. ISBN 978-1-107-02282-9.
cite-note-33. citerefkon1985Kon, Mark A. (1985). Probability Distributions in Quantum Statistical Mechanics. Springer. pp. 74, 84. ISBN 3-540-15690-9.
cite-note-44. citerefdriver2012Driver, Bruce (May 7, 2012). Analysis Tools with Examples (PDF). p. 327. Cf. Exercise 30.5.1.
cite-note-55. citerefsegalkunze1978Segal, Irving E.; Kunze, Ray A. (1978). Integrals and Operators (2nd revised and enlarged ed.). Springer. p. 106. ISBN 0-387-08323-5.
cite-note-66. citerefbogachevsmolyanov2020Bogachev, Vladimir I.; Smolyanov, Oleg G. (2020). Real and Functional Analysis. Moscow Lectures. Springer. p. 283. ISBN 978-3-030-38219-3. ISSN 2522-0314.
cite-note-77. citerefweaver2013Weaver, Nik (2013). Measure Theory and Functional Analysis. World Scientific. p. 142. ISBN 978-981-4508-56-8.
cite-note-88. citerefbhatia2009Bhatia, Rajendra (2009). Notes on Functional Analysis. Hindustan Book Agency. p. 149. ISBN 978-81-85931-89-0.
cite-note-99. citereffolland1999Folland, Gerald B. (1999). Real Analysis: Modern Techniques and Their Applications. Wiley. p. 187. ISBN 0-471-31716-0.
cite-note-1010. citerefrudin1987Rudin, Walter (1987). Real and complex analysis (3rd ed.). New York: McGraw-Hill. ISBN 0-07-054234-1.
cite-note-1111. citerefdouglas1998Douglas, Ronald G. (1998). Banach algebra techniques in operator theory (2nd ed.). New York Berlin Heidelberg: Springer. ISBN 0-387-98377-5.
cite-note-1212. Cf. citereftao2012Tao, Terence (2012). Topics in Random Matrix Theory. American Mathematical Society. p. 29. ISBN 978-0-8218-7430-1.
cite-note-1313. Cf. citereffreedman1971Freedman, David (1971). Markov Chains. Holden-Day. p. 1.
cite-note-1414. Cf. citerefchung1967Chung, Kai Lai (1967). Markov Chains with Stationary Transition Probabilities. Springer. p. 135.
cite-note-1515. citerefbrezisnirenberg1995Brezis, Haïm; Nirenberg, Louis (September 1995). "Degree theory and BMO. Part I: Compact manifolds without boundaries". Selecta Mathematica. 1 (2): 197–263. doi:10.1007/BF01671566.

• citerefwalter-rudin1974Walter Rudin (1974). Real and Complex Analysis (2nd ed.). McGraw-Hill. ISBN 978-0-07-054234-1.