Possible function y=f(x) if data points are given.

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Discussion Overview

The discussion revolves around obtaining a function y=f(x) that fits a set of given data points (x_{i},y_{i}). Participants explore various interpolation methods, particularly focusing on Lagrange's method and its derivation, while expressing uncertainty about the underlying principles and mathematical expressions involved.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant questions how to derive a function that satisfies given data points, expressing a desire to understand the 'why' and 'how' behind interpolation techniques.
  • Another participant explains Lagrange's method, proposing that a polynomial of degree N can be constructed using N+1 terms, each term designed to equal zero at N prescribed points and equal to a specific y-value at one point.
  • A participant expresses confusion regarding the expression for the polynomial provided by another, indicating a need for clarification on its derivation.
  • One suggestion involves considering a linear algebra approach to derive the inverse matrix for solving the polynomial interpolation problem.
  • Another participant elaborates on Lagrange's method, detailing the construction of the polynomial and how each term behaves at the given data points, emphasizing the role of the numerator and denominator in the fractions.
  • There is a request for a more general expression for Lagrange's polynomial and its derivation, indicating a lack of understanding of the explanation provided by another participant.

Areas of Agreement / Disagreement

Participants express varying levels of understanding regarding Lagrange's method and its derivation. While some provide explanations and insights, others indicate confusion and seek further clarification. No consensus is reached on the derivation of the polynomial or the general expression.

Contextual Notes

Participants acknowledge the complexity of deriving the polynomial expressions and the need for a deeper understanding of the underlying mathematical principles. Some assumptions about familiarity with interpolation techniques may not be shared among all participants.

s0ft
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How could one obtain a function [itex]y=f(x)[/itex] that satisfies a number of data points [itex](x_{i},y_{i})[/itex]. That is how would it be possible to get a function if some points that are to lie on it have been given? I've seen some lectures on interpolation but all I've gotten is only the way to solve problems, not the exact 'why' or 'how' these tecnhiques work.
Things like direct interpolation are straightforward but Lagrange's method, Newton's Divided Difference etc are the things I'm talking about.
 
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Let's take Lagrange's Method for interpolating polynomials.
What are we after? A polynomial of degree N that at (N+1) Points yields the prescribed y-values.

Let us think as follows:
If our polynomial consists of N+1 TERMS, each term having the property that they are Equal to zero at N of the prescribed Points, and Equal to a prescribed y-value, then it is easy to construct the Whole polynomial.

Suppose N=2
By Construction, we get p(x) easily as:
[tex]p(x)=\frac{(x-x_{2})(x-x_{3})}{(x_{1}-x_{2})(x_{1}-x_{3})}y_{1}+\frac{(x-x_{1})(x-x_{3})}{(x_{2}-x_{1})(x_{2}-x_{3})}y_{2}+\frac{(x-x_{1})(x-x_{2})}{(x_{3}-x_{1})(x_{3}-x_{2})}y_{3}[/tex]

If you focus on what each term here actually does, it is simple to generate the similar polynomials for other degrees of N
 
arildno said:
[tex]p(x)=\frac{(x-x_{2})(x-x_{3})}{(x_{1}-x_{2})(x_{1}-x_{3})}y_{1}+\frac{(x-x_{1})(x-x_{3})}{(x_{2}-x_{1})(x_{2}-x_{3})}y_{2}+\frac{(x-x_{1})(x-x_{2})}{(x_{3}-x_{1})(x_{3}-x_{2})}y_{3}[/tex]
Yes, I don't understand how that expression comes.
 
Hey s0ft.

You might want to consider a linear algebra version of the problem and then derive the inverse matrix.

Remember that if you are solving for a polynomial with n points, you will have n linearly independent equations to solve.
 
s0ft said:
Yes, I don't understand how that expression comes.
More generally, Lagrange's method for a n- 1 polynomial interpolating n given data points, [itex](x_i, y_i)[/itex], requires calculating n terms
[tex]\frac{(x- x_1)(x- x_2)\cdot\cdot\cdot(x-x_{i-1})(x- x_{i+1}\cdot\cdot\cdot(x- x_{n-1})(x- x_n)}{x_i- x_1)(x_i- x_2)\cdot\cdot\cdot(x_i- x_{i- 1})(x_i- x_{i+1}\cdot\cdot\cdot(x_i- x_{n-1})(x- x_n)}y_i[/tex]
with i from 1 to n, and adding them.

Look closely at that fraction. The numerator has the variable "x" minus each [itex]x_j[/itex] except the ith one. The denominator then has that [itex]x_i[/itex] minus each of the other [itex]x_j[/itex]. For x equal to each [itex]x_j[/itex] except [itex]x_i[/itex] that fraction will be 0 because there will be a factor of 0 in the numerator. For [itex]x= x_i[/itex] that fraction will be 1 because each factor in the numerator has an identical factor in the denominator.

That means that at [itex]x= x_i[/itex] will will have [itex]y_i[/itex] times 1 and all other y values times 0.

In the three point example arildno gave,
[tex]\frac{(x- x_2)(x- x_3)}{(x_1- x_2)(x_1- x_3)}y_1+ \frac{(x- x_1)(x- x_3)}{(x_2- x_1)(x_2- x_3)}y_2+ \frac{(x- x_1)(x- x_2)}{(x_3- x_1)(x_3- x_2)}y_3[/tex]
when [itex]x= x_1[/itex], the second and third fractions are 0 because of the [itex]x_1- x_1[/itex] in the numerator while the first fraction is
[tex]\frac{x_1- x_2)(x_1- x_3)}{(x_1- x_2)(x_1- x_3)}y_1= y_1[/tex]
and similarly for [itex]x= x_2[/itex] and [itex]x= x_3[/itex].
 
HallsOfIvy, probably you are thinking I know the general expression and presenting an explanation as to how what arildno posted came. Thank you for your effort but if I understood you correctly, it is the most general expression for the Lagrange's polynomial whose derivation I'm after.
 

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