Essential range
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
top
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
• Examples
• See also
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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
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
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:
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
cite-note-11. ↑ citerefzimmer1990Zimmer, Robert J. (1990). Essential Results of Functional Analysis. University of Chicago Press. p. 2. ISBN 0-226-98337-4.
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-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-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-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.
• citerefwalter-rudin1974Walter Rudin (1974). Real and Complex Analysis (2nd ed.). McGraw-Hill. ISBN 978-0-07-054234-1.