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'.

Formal definition

Let (X,𝒜,μ) be a measure space, and let (Y,𝒯) be a topological space. For any (𝒜,σ(𝒯))-measurable f:XY, we say the essential range of f to mean the set

ess.im(f)={yY0<μ(f1(U)) for all U𝒯 with yU}.[1](Example 0.A.5)[2][3]

Equivalently, ess.im(f)=supp(f*μ), where f*μ is the pushforward measure onto σ(𝒯) of μ under f and supp(f*μ) denotes the support of f*μ.[4]

Essential values

We sometimes use the phrase "essential value of f" to mean an element of the essential range of f.[5](Exercise 4.1.6)[6](Example 7.1.11)

Special cases of common interest

Y = C

Say (Y,𝒯) is equipped with its usual topology. Then the essential range of f is given by

ess.im(f)={zfor all ε>0:0<μ{xX:|f(x)z|<ε}}.[7](Definition 4.36)[8][9](cf. Exercise 6.11)

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) is discrete, i.e., 𝒯=𝒫(Y) is the power set of Y, i.e., the discrete topology on Y. Then the essential range of f is the set of values y in Y with strictly positive f*μ-measure:

ess.im(f)={yY:0<μ(fpre{y})}={yY:0<(f*μ){y}}.[10](Example 1.1.29)[11][12]

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).
  • The essential image cannot be used to distinguish functions that are almost everywhere equal: If f=g holds μ-almost everywhere, then ess.im(f)=ess.im(g).
  • These two facts characterise the essential image: It is the biggest set contained in the closures of im(g) for all g that are a.e. equal to f:
ess.im(f)=f=ga.e.im(g).
  • The essential range satisfies AX:f(A)ess.im(f)=μ(A)=0.
  • This fact characterises the essential image: It is the smallest closed subset of 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(f) where f is considered as an element of the C*-algebra L(μ).

Examples

  • If μ 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 Xn is open, f:X continuous and μ the Lebesgue measure, then ess.im(f)=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:XY, where Y is a separable metric space. If X and Y are differentiable manifolds of the same dimension, if f VMO(X,Y) and if ess.im(f)Y, then degf=0.[13]

See also

References

  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. p. 327. https://mathweb.ucsd.edu/~bdriver/240C-S2018/Lecture_Notes/2012%20Notes/240Lecture_Notes_Ver8.pdf.  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. 
  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. Cf. Tao, Terence (2012). Topics in Random Matrix Theory. American Mathematical Society. p. 29. ISBN 978-0-8218-7430-1. 
  11. Cf. Freedman, David (1971). Markov Chains. Holden-Day. p. 1. 
  12. Cf. Chung, Kai Lai (1967). Markov Chains with Stationary Transition Probabilities. Springer. p. 135. 
  13. 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.