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Essential range

From Wikipedia, the free encyclopedia

Inmathematics, particularlymeasure theory, theessential range, or the set ofessential values, of afunction is intuitively the 'non-negligible' range of the function: It does not change between two functions that are equalalmost everywhere. One way of thinking of the essential range of a function is theset on which the range of the function is 'concentrated'.

Formal definition

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Let(X,A,μ){\displaystyle (X,{\cal {A}},\mu )} be ameasure space, and let(Y,T){\displaystyle (Y,{\cal {T}})} be atopological space. For any(A,σ(T)){\displaystyle ({\cal {A}},\sigma ({\cal {T}}))}-measurable functionf:XY{\displaystyle f:X\to Y}, we say theessential range off{\displaystyle f} to mean the set

ess.im(f)={yY0<μ(f1(U)) for all UT with yU}.{\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,ess.im(f)=supp(fμ){\displaystyle \operatorname {ess.im} (f)=\operatorname {supp} (f_{*}\mu )}, wherefμ{\displaystyle f_{*}\mu } is thepushforward measure ontoσ(T){\displaystyle \sigma ({\cal {T}})} ofμ{\displaystyle \mu } underf{\displaystyle f} andsupp(fμ){\displaystyle \operatorname {supp} (f_{*}\mu )} denotes thesupport offμ.{\displaystyle f_{*}\mu .}[4]

Essential values

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The phrase "essential value off{\displaystyle f}" is sometimes used to mean an element of the essential range off.{\displaystyle f.}[5]: Exercise 4.1.6 [6]: Example 7.1.11 

Special cases of common interest

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Y =C

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

ess.im(f)={zCfor all εR>0:0<μ{xX:|f(x)z|<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\}.}[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 numbersz such that the inverse image of each ε-neighbourhood ofz underf has positive measure.

(Y,T) is discrete

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Say(Y,T){\displaystyle (Y,{\cal {T}})} isdiscrete, i.e.,T=P(Y){\displaystyle {\cal {T}}={\cal {P}}(Y)} is thepower set ofY,{\displaystyle Y,} i.e., thediscrete topology onY.{\displaystyle Y.} Then the essential range off is the set of valuesy inY with strictly positivefμ{\displaystyle f_{*}\mu }-measure:

ess.im(f)={yY:0<μ(fpre{y})}={yY:0<(fμ){y}}.{\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

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ess.im(f)=f=ga.e.im(g)¯{\displaystyle \operatorname {ess.im} (f)=\bigcap _{f=g\,{\text{a.e.}}}{\overline {\operatorname {im} (g)}}}.

Examples

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Extension

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The notion of essential range can be extended to the case off:XY{\displaystyle f:X\to Y}, whereY{\displaystyle Y} is aseparablemetric space.IfX{\displaystyle X} andY{\displaystyle Y} aredifferentiable manifolds of the same dimension, iff{\displaystyle f\in }VMO(X,Y){\displaystyle (X,Y)} and ifess.im(f)Y{\displaystyle \operatorname {ess.im} (f)\neq Y}, thendegf=0{\displaystyle \deg f=0}.[15]

See also

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References

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  1. ^Zimmer, Robert J. (1990).Essential Results of Functional Analysis. University of Chicago Press. p. 2.ISBN 0-226-98337-4.
  2. ^Kuksin, Sergei; Shirikyan, Armen (2012).Mathematics of Two-Dimensional Turbulence. Cambridge University Press. p. 292.ISBN 978-1-107-02282-9.
  3. ^Kon, Mark A. (1985).Probability Distributions in Quantum Statistical Mechanics. Springer. pp. 74, 84.ISBN 3-540-15690-9.
  4. ^Driver, Bruce (May 7, 2012).Analysis Tools with Examples(PDF). p. 327. Cf. Exercise 30.5.1.
  5. ^Segal, Irving E.;Kunze, Ray A. (1978).Integrals and Operators (2nd revised and enlarged ed.). Springer. p. 106.ISBN 0-387-08323-5.
  6. ^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.
  7. ^Weaver, Nik (2013).Measure Theory and Functional Analysis. World Scientific. p. 142.ISBN 978-981-4508-56-8.
  8. ^Bhatia, Rajendra (2009).Notes on Functional Analysis. Hindustan Book Agency. p. 149.ISBN 978-81-85931-89-0.
  9. ^Folland, Gerald B. (1999).Real Analysis: Modern Techniques and Their Applications. Wiley. p. 187.ISBN 0-471-31716-0.
  10. ^Rudin, Walter (1987).Real and complex analysis (3rd ed.). New York: McGraw-Hill.ISBN 0-07-054234-1.
  11. ^Douglas, Ronald G. (1998).Banach algebra techniques in operator theory (2nd ed.). New York Berlin Heidelberg: Springer.ISBN 0-387-98377-5.
  12. ^Cf.Tao, Terence (2012).Topics in Random Matrix Theory. American Mathematical Society. p. 29.ISBN 978-0-8218-7430-1.
  13. ^Cf.Freedman, David (1971).Markov Chains. Holden-Day. p. 1.
  14. ^Cf.Chung, Kai Lai (1967).Markov Chains with Stationary Transition Probabilities. Springer. p. 135.
  15. ^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.
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