Essential range
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'.
Formal definition
[edit ]Let {\displaystyle (X,{\cal {A}},\mu )} be a measure space, and let {\displaystyle (Y,{\cal {T}})} be a topological space. For any {\displaystyle ({\cal {A}},\sigma ({\cal {T}}))}-measurable function {\displaystyle f:X\to Y}, we say the essential range of {\displaystyle f} to mean the set
- {\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\}.}[1] : Example 0.A.5 [2] [3]
Equivalently, {\displaystyle \operatorname {ess.im} (f)=\operatorname {supp} (f_{*}\mu )}, where {\displaystyle f_{*}\mu } is the pushforward measure onto {\displaystyle \sigma ({\cal {T}})} of {\displaystyle \mu } under {\displaystyle f} and {\displaystyle \operatorname {supp} (f_{*}\mu )} denotes the support of {\displaystyle f_{*}\mu .}[4]
Essential values
[edit ]The phrase "essential value of {\displaystyle f}" is sometimes used to mean an element of the essential range of {\displaystyle f.}[5] : Exercise 4.1.6 [6] : Example 7.1.11
Special cases of common interest
[edit ]Y = C
[edit ]Say {\displaystyle (Y,{\cal {T}})} is {\displaystyle \mathbb {C} } equipped with its usual topology. Then the essential range of f is given by
- {\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\}.}[7] : Definition 4.36 [8] [9] : cf. Exercise 6.11 [10] : Exercise 3.19 [11] : Definition 2.61
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
[edit ]Say {\displaystyle (Y,{\cal {T}})} is discrete, i.e., {\displaystyle {\cal {T}}={\cal {P}}(Y)} is the power set of {\displaystyle Y,} i.e., the discrete topology on {\displaystyle Y.} Then the essential range of f is the set of values y in Y with strictly positive {\displaystyle f_{*}\mu }-measure:
- {\displaystyle \operatorname {ess.im} (f)=\{y\in Y:0<\mu (f^{\text{pre}}\{y\})\}=\{y\in Y:0<(f_{*}\mu )\{y\}\}.}[12] : Example 1.1.29 [13] [14]
Properties
[edit ]- 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 {\displaystyle {\overline {\operatorname {im} (f)}}}.
- The essential image cannot be used to distinguish functions that are almost everywhere equal: If {\displaystyle f=g} holds {\displaystyle \mu }-almost everywhere, then {\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 {\displaystyle \operatorname {im} (g)} for all g that are a.e. equal to f:
- {\displaystyle \operatorname {ess.im} (f)=\bigcap _{f=g,円{\text{a.e.}}}{\overline {\operatorname {im} (g)}}}.
- The essential range satisfies {\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 {\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 {\displaystyle \sigma (f)} where f is considered as an element of the C*-algebra {\displaystyle L^{\infty }(\mu )}.
Examples
[edit ]- If {\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 {\displaystyle X\subseteq \mathbb {R} ^{n}} is open, {\displaystyle f:X\to \mathbb {C} } continuous and {\displaystyle \mu } the Lebesgue measure, then {\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
[edit ]The notion of essential range can be extended to the case of {\displaystyle f:X\to Y}, where {\displaystyle Y} is a separable metric space. If {\displaystyle X} and {\displaystyle Y} are differentiable manifolds of the same dimension, if {\displaystyle f\in } VMO {\displaystyle (X,Y)} and if {\displaystyle \operatorname {ess.im} (f)\neq Y}, then {\displaystyle \deg f=0}.[15]
See also
[edit ]References
[edit ]- ^ Zimmer, Robert J. (1990). Essential Results of Functional Analysis. University of Chicago Press. p. 2. ISBN 0-226-98337-4.
- ^ Kuksin, Sergei; Shirikyan, Armen (2012). Mathematics of Two-Dimensional Turbulence. Cambridge University Press. p. 292. ISBN 978-1-107-02282-9.
- ^ Kon, Mark A. (1985). Probability Distributions in Quantum Statistical Mechanics. Springer. pp. 74, 84. ISBN 3-540-15690-9.
- ^ Driver, Bruce (May 7, 2012). Analysis Tools with Examples (PDF). p. 327. Cf. Exercise 30.5.1.
- ^ Segal, Irving E.; Kunze, Ray A. (1978). Integrals and Operators (2nd revised and enlarged ed.). Springer. p. 106. ISBN 0-387-08323-5.
- ^ Bogachev, Vladimir I.; Smolyanov, Oleg G. (2020). Real and Functional Analysis. Moscow Lectures. Springer. p. 283. ISBN 978-3-030-38219-3. ISSN 2522-0314.
- ^ Weaver, Nik (2013). Measure Theory and Functional Analysis. World Scientific. p. 142. ISBN 978-981-4508-56-8.
- ^ Bhatia, Rajendra (2009). Notes on Functional Analysis. Hindustan Book Agency. p. 149. ISBN 978-81-85931-89-0.
- ^ Folland, Gerald B. (1999). Real Analysis: Modern Techniques and Their Applications. Wiley. p. 187. ISBN 0-471-31716-0.
- ^ Rudin, Walter (1987). Real and complex analysis (3rd ed.). New York: McGraw-Hill. ISBN 0-07-054234-1.
- ^ Douglas, Ronald G. (1998). Banach algebra techniques in operator theory (2nd ed.). New York Berlin Heidelberg: Springer. ISBN 0-387-98377-5.
- ^ Cf. Tao, Terence (2012). Topics in Random Matrix Theory. American Mathematical Society. p. 29. ISBN 978-0-8218-7430-1.
- ^ Cf. Freedman, David (1971). Markov Chains. Holden-Day. p. 1.
- ^ Cf. Chung, Kai Lai (1967). Markov Chains with Stationary Transition Probabilities. Springer. p. 135.
- ^ Brezis, 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.
- Walter Rudin (1974). Real and Complex Analysis (2nd ed.). McGraw-Hill. ISBN 978-0-07-054234-1.