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<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  G.G. Lorentz,  "Approximation of functions" , Holt, Rinehart &amp; Winston  (1966)</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top">  A. Pinkus,  "<img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/a/a012/a012990/a01299047.png" />-widths in approximation theory" , Springer  (1985)  (Translated from Russian)</TD></TR></table>
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<TR><TD valign="top">[a1]</TD> <TD valign="top">  G.G. Lorentz,  "Approximation of functions" , Holt, Rinehart &amp; Winston  (1966)</TD></TR>
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<TR><TD valign="top">[a2]</TD> <TD valign="top">  A. Pinkus,  "$n$-widths in approximation theory" , Springer  (1985)  (Translated from Russian)</TD></TR>
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Latest revision as of 07:22, 26 March 2023


A quantitative expression for the error of an approximation. When the discussion is about the approximation of a function $ f $ by a function $ \phi $, the measure of approximation $ \mu (f, \phi ) $ is usually defined by the metric in a function space containing both $ f $ and $ \phi $. For example, if $ f $ and $ \phi $ are continuous functions on a segment $ [a, b] $, the uniform metric of $ C [a, b] $ is commonly used, i.e. one puts

$$ \mu (f, \phi ) = \ \max _ {a \leq t \leq b } \ | f (t) - \phi (t) | . $$

If continuity of the approximated function is not guaranteed or if the conditions of the problem imply that it is important that $ f $ and $ \phi $ are close on $ [a, b] $ in an average sense, the integral metric of a space $ L _ {p} [a, b] $ may be used, putting

$$ \mu (f, \phi ) = \ \int\limits _ { a } ^ { b } q (t) | f (t) - \phi (t) | ^ {p} \ dt,\ p > 0, $$

where $ q (t) $ is a weight function. The case $ p = 2 $ is most often used and is most convenient from a practical point of view (cf. Mean-square approximation of a function).

The measure of approximation may take into account only values of $ f $ and $ \phi $ in discrete points $ t _ {k} $, $ k = 1 \dots n $, of $ [a, b] $, e.g.

$$ \mu (f, \phi ) = \ \max _ {1 \leq k \leq n } \ | f (t _ {k} ) - \phi (t _ {k} ) | , $$

$$ \mu (f, \phi ) = \sum _ {k = 1 } ^ { n } q _ {k} | f (t _ {k} ) - \phi (t _ {k} ) | ^ {p} , $$

where $ q _ {k} $ are certain positive coefficients.

One defines in an analogous way the measure of approximation of functions in two or more variables.

The measure of approximation of a function $ f $ by a family $ F $ of functions is usually taken to be the best approximation:

$$ E (f, F) = \ \mu (f, F) = \ \inf _ {\phi \in F } \ \mu (f, \phi ). $$

The quantity

$$ E ( \mathfrak M , F) = \ \mu ( \mathfrak M , F) = \ \sup _ {f \in \mathfrak M } \ \inf _ {\phi \in F } \ \mu (f, \phi ) $$

is usually taken as the measure of approximation of a class $ \mathfrak M $ of functions $ f $ by functions $ \phi $ from a certain fixed set $ F $. It characterizes the maximal deviation of functions in $ \mathfrak M $ from functions in $ F $ that are closest to them.

In general, when approximation in an arbitrary metric space $ X $ is considered, the measure of approximation $ \mu (x, u) $ of an element $ x $ by an element $ u $( a set $ F $) is the distance $ \rho (x, u) $( or $ \rho (x, F) $) between $ x $ and $ u $( or $ F $) in the metric of $ X $.

References

[1] V.L. Goncharov, "The theory of interpolation and approximation of functions" , Moscow (1954) (In Russian)
[2] S.M. Nikol'skii, "Approximation of functions of several variables and imbedding theorems" , Springer (1975) (Translated from Russian)
[3] J.R. Rice, "The approximation of functions" , 1–2 , Addison-Wesley (1964–1968)

Comments

The measure of approximation is also called the error measure.

References

[a1] G.G. Lorentz, "Approximation of functions" , Holt, Rinehart & Winston (1966)
[a2] A. Pinkus, "$n$-widths in approximation theory" , Springer (1985) (Translated from Russian)
How to Cite This Entry:
Approximation of functions, measure of. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Approximation_of_functions,_measure_of&oldid=53270
This article was adapted from an original article by N.P. KorneichukV.P. Motornyi (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article