Connexions

You are here: Home » Content » Lagrange Interpolation
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Digital Filter Design"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Lagrange Interpolation

Module by: Douglas L. Jones

Summary: Lagrange's interpolation formula is a simple and clever method for finding the unique polynomial of order L that exactly passes through L+1 distinct samples of a signal.

Lagrange's interpolation method is a simple and clever way of finding the unique LLth-order polynomial that exactly passes through L+1 L 1 distinct samples of a signal. Once the polynomial is known, its value can easily be interpolated at any point using the polynomial equation. Lagrange interpolation is useful in many applications, including Parks-McClellan FIR Filter Design.

Lagrange interpolation formula

Given an LLth-order polynomial Px= a 0 + a 1 x+...+ a L xL=k=0L a k xk P x a 0 a 1 x ... a L x L k 0 L a k x k and L+1 L 1 values of P x k P x k at different x k x k , k01...L k 0 1 ... L , x i x j x i x j , ij i j , the polynomial can be written as Px=k=0LP x k x- x 1 x- x 2 ...x- x k - 1 x- x k + 1 ...x- x L x k - x 1 x k - x 2 ... x k - x k - 1 x k - x k + 1 ... x k - x L P x k 0 L P x k x x 1 x x 2 ... x x k - 1 x x k + 1 ... x x L x k x 1 x k x 2 ... x k x k - 1 x k x k + 1 ... x k x L The value of this polynomial at other xx can be computed via substitution into this formula, or by expanding this formula to determine the polynomial coefficients a k a k in standard form.

Proof

Note that for each term in the Lagrange interpolation formula above, i=0,ikLx- x i x k - x i =1ifx= x k 0ifx= x j jk i 0, i k L x x i x k x i 1 x x k 0 x x j j k and that it is an L Lth-order polynomial in x x. The Lagrange interpolation formula is thus exactly equal to P x k P x k at all x k x k , and as a sum of LLth-order polynomials is itself an LLth-order polynomial.
It can be shown that the Vandermonde matrix 1 x 0 x 0 2... x 0 L1 x 1 x 1 2... x 1 L1 x 2 x 2 2... x 2 L1 x L x L 2... x L L a 0 a 1 a 2 a L =P x 0 P x 1 P x 2 P x L 1 x 0 x 0 2 ... x 0 L 1 x 1 x 1 2 ... x 1 L 1 x 2 x 2 2 ... x 2 L 1 x L x L 2 ... x L L a 0 a 1 a 2 a L P x 0 P x 1 P x 2 P x L has a non-zero determinant and is thus invertible, so the L Lth-order polynomial passing through all L+1 L 1 sample points x j x j is unique. Thus the Lagrange polynomial expressions, as an L Lth-order polynomial passing through the L+1 L 1 sample points, must be the unique Px P x .

Comments, questions, feedback, criticisms?

Send feedback