Convergence of polynomial interpolation

In summary: We know that such a point exists because the sup norm is the maximum absolute value of a function on a given interval. Since |f(x) - pn(x)| < 2ε for all n > N, we can conclude that ||f - pn|| < 2ε for all n > N. This means that the sequence {pn} converges to f in the sup norm, as desired.In summary, we have shown that if a sequence of functions {pn} is Cauchy with respect to the sup norm, then it converges to f in the sup norm. This is due to
  • #1
Milky
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Homework Statement


Let f be a contintuous real valued function on a closed interval [a, b]. The sup norm of such a function is maxx|f(x)|

Let pn be a polynomial interpolation of f determined by n points [xi, f(xi)] where xi Ε [a, b] for every i.
Suppose that the sequence {pn} is Cauchy with respect to the sup norm. Can you prove that pn --> f in sup norm? What can you say about this problem.


Homework Equations





The Attempt at a Solution


Not yet given, I am unsure how to even approach this problem and was hoping someone had ideas?
 
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  • #2



Hello,

Thank you for your forum post. This is an interesting problem and I would be happy to offer some guidance.

First, let's define what it means for a sequence of functions to be Cauchy with respect to a norm. A sequence of functions {fn} is said to be Cauchy with respect to a norm if for any given ε > 0, there exists an integer N such that for all n, m > N, ||fn - fm|| < ε, where ||·|| represents the norm.

In this case, our norm is the sup norm, which is defined as the maximum absolute value of a function on a given interval. So, given a function f, the sup norm is max|f(x)| for x in the interval [a, b].

Now, let's consider the given sequence {pn} that is Cauchy with respect to the sup norm. This means that for any given ε > 0, there exists an integer N such that for all n, m > N, ||pn - pm|| < ε. We want to prove that this sequence converges to f in the sup norm, which means that for any given ε > 0, there exists an integer N such that for all n > N, ||pn - f|| < ε.

To prove this, we can use the fact that {pn} is Cauchy and the continuity of f. Since f is continuous on the closed interval [a, b], we know that it is uniformly continuous. This means that for any given ε > 0, there exists a δ > 0 such that for all x, y in [a, b], if |x - y| < δ, then |f(x) - f(y)| < ε.

Now, let's choose ε > 0 and let N be the integer such that for all n, m > N, ||pn - pm|| < ε. Since {pn} is Cauchy, we know that for any given x in [a, b], |pn(x) - pm(x)| < ε for all n, m > N. So, for any given x in [a, b], we have |f(x) - pn(x)| < ε and |f(x) - pm(x)| < ε. This means that |pn(x) - pm(x)| < 2ε for all n, m > N.

Now, let's
 

Related to Convergence of polynomial interpolation

What is polynomial interpolation?

Polynomial interpolation is a mathematical method used to find a polynomial function that passes through a given set of points. It is commonly used in mathematics and engineering to approximate a function or to fill in missing data points.

How does polynomial interpolation work?

The basic idea behind polynomial interpolation is to construct a polynomial function of degree n that satisfies n+1 given points. This is done by setting up a system of equations using the given points and their corresponding polynomial values. The unknown coefficients of the polynomial can then be solved for using this system of equations.

What is the significance of the convergence of polynomial interpolation?

The convergence of polynomial interpolation refers to the behavior of the polynomial function as the number of data points increases. If the number of data points is large enough, the polynomial function will converge to the true function being approximated. This is important because it ensures that the polynomial interpolation is a valid approximation of the function.

What factors affect the convergence of polynomial interpolation?

The convergence of polynomial interpolation is affected by the degree of the polynomial being used, the spacing of the data points, and the smoothness of the function being approximated. Higher degree polynomials may have faster convergence, while closer spacing of data points and smoother functions typically result in better convergence.

What are some limitations of polynomial interpolation?

Polynomial interpolation can produce inaccurate results when used with large degrees of polynomials or when data points are not evenly spaced. It also cannot be used to interpolate functions with sharp changes or discontinuities. Additionally, polynomial interpolation can be computationally expensive for large data sets.

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