Lebesgue constant (interpolation)

Lebesgue constant (interpolation)

:"For other uses, see: Lebesgue constant."

In mathematics, the Lebesgue constants (depending on a set of nodes and of its size) give an idea of how good the interpolant of a function (at the given nodes) is in comparison with the best polynomial approximation of the function (the degree of the polynomials are obviously fixed). The Lebesgue function for polynomials of degree at most "n" and for the set of ("n"+1) nodes "T" is generally denoted by Λ"n"("T"). These constants are named after Henri Lebesgue.


We fix the interpolation nodes "x"0, …, "x""n" and an interval ["a", "b"] containing all the interpolation nodes. The process of interpolation maps the function "f" to a polynomial "p". This defines a mapping "X" from the space "C"( ["a", "b"] ) of all continuous functions on ["a", "b"] to itself. The map "X" is linear and it is a projection on the subspace Π"n" of polynomials of degree "n" or less.

The Lebesgue constant Λ"n"("T") is defined as the operator norm of "X". This definition requires us to specify a norm on "C"( ["a", "b"] ). The maximum norm is usually the most convenient.


The Lebesgue constant bounds the interpolation error:: |f-X(f)| le (Lambda_n(T)+1) |f-p^*|.

We will here prove this statement with the maximum norm. Let "p"∗ denote the best approximation of "f" among the polynomials of degree "n" or less. In other words, "p"∗ minimizes ||"p"−"f"|| among all "p" in Π"n". Then

: | f-X(f) | le | f-p^{ast} | + | p^{ast} - X(f) | ,!

by the triangle inequality. But "X" is a projection on Π"n", so "p"∗−"X"("f") = "X"("p"∗−"f"). This finishes the proof. Note that this relation comes also as a special case of Lebesgue's lemma.

In other words, the interpolation polynomial is at most a factor Λ"n"("T")+1 worse than the best possible approximation. This suggests that we look for a set of interpolation nodes with a small Lebesgue constant.

The Lebesgue constant can be expressed in terms of the Lagrange basis polynomials::l_j(x) := prod_{egin{smallmatrix}i=0\ j eq iend{smallmatrix^{n} frac{x-x_i}{x_j-x_i} In fact, we have the Lebesgue function: lambda_n(x) = sum_{j=0}^n |l_j(x)|. and the Lebesgue constant (or Lebesgue number) for the grid is its maximum value:Lambda_n(T)=max_{xin [a,b] } lambda_n(x) Nevertheless, it is not easy to find an explicit expression for Λ"n"("T").

Minimal Lebesgue constants

In the case of equidistant nodes, the Lebesgue constant grows exponentially. More precisely, we have the following asymptotic estimate: Lambda_n(T) sim frac{2^{n+1{e , n log n} quadmbox{as}quad n o infty. On the other hand, the Lebesgue constant grows only logarithmically if Chebyshev nodes are used, since we have: frac{2}{pi} log(n+1)+a < Lambda_n(T) < frac{2}{pi} log(n+1) + 1,where "a" = 0.9625&hellip;.

We conclude again that Chebyshev nodes are a very good choice for polynomial interpolation. However, there is an easy (linear) transformation of Chebyshev nodes that gives a better Lebesgue constant. Let "t""i" denote the "i"th Chebyshev node. Then, define "s""i" = "t""i"cos(&pi;&frasl;2("n"+1)). For such nodes::Lambda_n(S)where "b" = 0.7219&hellip;.

Those nodes are, however, not optimal (i.e. they do not minimize the Lebesgue constants) and the search for an optimal set of nodes (which has already been proved to be unique under some assumptions) is still one of the most intriguing topics in mathematics today. Using a computer, one can approximate the values of the minimal constants, here for the canonical interval [−1,1] :


There are several sets of nodes that minimize, for fixed "n", the Lebesgue constant. Though if we assume that we always take −1 and 1 as nodes for interpolation, then such a set is unique. To illustrate this property, we shall see what happens when "n"=2 (i.e. we consider 3 interpolation nodes in which case the property is not trivial). One can check that each set of nodes of type (−"a",0,"a") is optimal when &radic;8&frasl;3 &le; "a" &le; 1 (we consider only nodes in [−1,1] ). If we force the set of nodes to be of the type (−1,"b",1), then "b" must equal 0 (look at the Lebesgue function, whose maximum is the Lebesgue constant).

ensitivity of the values of a polynomial

The Lebesgue constants also arise in another problem. Let "p" be a polynomial of degree "n" expressed in the Lagrangian form associated with the points in the vector "t" (i.e. the vector "u" of its coefficients is the vector containing the values "p"("t""i")). Let hat{p} be a polynomial obtained by slightly changing the coefficients "u" of the original polynomial "p". Let us consider the equation:: frac{|p-hat{p}{|p\leq Lambda_n(t)frac{|u-hat{u}{|uThis means that the (relative) error in the values of hat{p} will not be higher than the appropriate Lebesgue constant times the relative error in the coefficients. In this sense, the Lebesgue constant can be viewed as the relative condition number of the operator mapping each coefficient vector "u" to the set of the values of the polynomial with coefficients "u" in the Lagrange form. We can actually define such an operator for each polynomial basis but its condition number is greater than the optimal Lebesgue constant for most convenient bases.


* Citation
last = Brutman
first = L.
title = Lebesgue functions for polynomial interpolation &mdash; a survey
journal = Annals of Numerical Mathematics
volume = 4
year = 1997
pages = 111–127
issn = 1021-2655

* Citation
last = Smith
first = Simon J.
title = Lebesgue constants in polynomial interpolation
journal = Annales Mathematicae et Informaticae
volume = 33
year = 2006
pages = 109–123
issn = 1787-5021
url = http://www.ektf.hu/tanszek/matematika/ami/2006/2006.htm

* [http://mathworld.wolfram.com/LebesgueConstants.html Lebesgue constants] on MathWorld.

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