Derivation of Lagrange Family of Interpolation functions

In summary, the conversation discusses the determination of linear interpolation functions based on a given equation and asks for clarification on the use of ellipses and the specific solution for ψ1(ξ). The dots in the equation represent filling in the blanks with a continuing pattern, while the expression for ψ1(ξ) is dependent on the value of n in the equation.
  • #1
bugatti79
794
1
Folks,

I am puzzled how the linear interpolation functions (see attached) were determined based on the following equation below

##\displaystyle \psi_i=\frac{(\xi-\xi_1)(\xi-\xi_2)...(\xi-\xi_{i-1})(\xi-\xi_{i+1})...(\xi-\xi_n)}{(\xi_i-\xi_1)(\xi_i-\xi_2)...(\xi_i-\xi_{i-1})(\xi_i-\xi_{i+1})...(\xi_i-\xi_n)}##

What do the dots represent above?

and

[itex]\psi_i(\xi_j)= 1[/itex] if ##i=j## and ##0## if ##i\ne j##

For example how is ##\psi_1(\xi)## determined?

thanks
 

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  • #2
bugatti79 said:
Folks,

I am puzzled how the linear interpolation functions (see attached) were determined based on the following equation below

##\displaystyle \psi_i=\frac{(\xi-\xi_1)(\xi-\xi_2)...(\xi-\xi_{i-1})(\xi-\xi_{i+1})...(\xi-\xi_n)}{(\xi_i-\xi_1)(\xi_i-\xi_2)...(\xi_i-\xi_{i-1})(\xi_i-\xi_{i+1})...(\xi_i-\xi_n)}##

What do the dots represent above?

and

[itex]\psi_i(\xi_j)= 1[/itex] if ##i=j## and ##0## if ##i\ne j##

For example how is ##\psi_1(\xi)## determined?


thanks

The dots mean fill in all the like terms for indices between the given. In this case, 3 to i-2 and i+2 to n-1.

For the second question, when j = i, the numerator and denominator are the same. When j ≠ i, one term of the numerator product = 0.
 
  • #3
mathman said:
The dots mean fill in all the like terms for indices between the given. In this case, 3 to i-2 and i+2 to n-1.

For the second question, when j = i, the numerator and denominator are the same. When j ≠ i, one term of the numerator product = 0.

Sorry, I still don't follow...

I don't see how ##\psi_1(\xi)=\frac{1}{2}(1-\xi)## is obtained...?
 
  • #4
bugatti79 said:
Folks,

I am puzzled how the linear interpolation functions (see attached) were determined based on the following equation below

##\displaystyle \psi_i=\frac{(\xi-\xi_1)(\xi-\xi_2)...(\xi-\xi_{i-1})(\xi-\xi_{i+1})...(\xi-\xi_n)}{(\xi_i-\xi_1)(\xi_i-\xi_2)...(\xi_i-\xi_{i-1})(\xi_i-\xi_{i+1})...(\xi_i-\xi_n)}##

What do the dots represent above?
The dots (...) are called an ellipsis, and appear as three periods. The ellipsis means "continuing in the same fashion." The first missing factor in both the numerator and denominator would be (x - x3) and the next would be (x - x4), and so on. (I don't see any purpose in writing ##\xi## when plain old x will do just fine.)
 
  • #5
bugatti79 said:
I am puzzled how the linear interpolation functions (see attached) were determined based on the following equation below

##\displaystyle \psi_i=\frac{(\xi-\xi_1)(\xi-\xi_2)...(\xi-\xi_{i-1})(\xi-\xi_{i+1})...(\xi-\xi_n)}{(\xi_i-\xi_1)(\xi_i-\xi_2)...(\xi_i-\xi_{i-1})(\xi_i-\xi_{i+1})...(\xi_i-\xi_n)}##

What do the dots represent above?

like any other dots like that which you see in a mathematical expression. it means "fill in the blank" with the continuing pattern.

it's not as good as my Applied Engineering Mathematics book from Kreyszig, but the wikipedia article on Lagrange polynomials should have the answers to your question.
 
  • #6
bugatti79 said:
Folks,



##\displaystyle \psi_i=\frac{(\xi-\xi_1)(\xi-\xi_2)...(\xi-\xi_{i-1})(\xi-\xi_{i+1})...(\xi-\xi_n)}{(\xi_i-\xi_1)(\xi_i-\xi_2)...(\xi_i-\xi_{i-1})(\xi_i-\xi_{i+1})...(\xi_i-\xi_n)}##



##\psi_1(\xi)=\frac{1}{2}(1-\xi)## is obtained...?

##\displaystyle \psi_1=\frac{(\xi-\xi_1)(\xi-\xi_2)(\xi-\xi_{0})(\xi-\xi_{2})(\xi-\xi_1)}{(\xi_1-\xi_1)(\xi_1-\xi_2)(\xi_1-\xi_{0})(\xi_1-\xi_{2})(\xi_1-\xi_1)}##...?
 
  • #7
bugatti79 said:
##\displaystyle \psi_1=\frac{(\xi-\xi_1)(\xi-\xi_2)(\xi-\xi_{0})(\xi-\xi_{2})(\xi-\xi_1)}{(\xi_1-\xi_1)(\xi_1-\xi_2)(\xi_1-\xi_{0})(\xi_1-\xi_{2})(\xi_1-\xi_1)}##...?

ξ1 - ξ1 is NOT a term in the denominator. Look at the definition of ψi, the ith term is left out of the numerator and denominator.

Where did you get the expression for ψ1(ξ)?
 
  • #8
mathman said:
ξ1 - ξ1 is NOT a term in the denominator. Look at the definition of ψi, the ith term is left out of the numerator and denominator.

Where did you get the expression for ψ1(ξ)?

Its the first equation shown in the picture in first post...
 
  • #9
bugatti79 said:
Its the first equation shown in the picture in first post...

The pictures are slightly criptic. The important thing is that the definition of ψi depends on n. The particular item you ask about is for n = 2.
In that case:
ψ1(ξ) = (ξ-ξ2)/(ξ12), where ξ1= -1 and ξ2=+1.
 
  • #10
mathman said:
The pictures are slightly criptic. The important thing is that the definition of ψi depends on n. The particular item you ask about is for n = 2.
In that case:
ψ1(ξ) = (ξ-ξ2)/(ξ12), where ξ1= -1 and ξ2=+1.

OK, thanks
 

Related to Derivation of Lagrange Family of Interpolation functions

What is the derivation of Lagrange Family of Interpolation functions?

The Lagrange Family of Interpolation functions is a set of mathematical equations used to approximate a function based on a given set of data points. It is named after mathematician Joseph-Louis Lagrange, who first developed the method in the 18th century.

Why is the Lagrange Family of Interpolation functions important?

This method is important because it allows us to approximate a function at any point within a given range, not just at the given data points. This is useful in many fields, including engineering, physics, and data analysis.

How do you derive the Lagrange Family of Interpolation functions?

The derivation of the Lagrange Family of Interpolation functions involves using a system of equations to solve for the coefficients of the polynomial that best fits the given data points. This involves finding the values of the Lagrange basis polynomials, which are used to construct the final interpolation function.

What are the limitations of the Lagrange Family of Interpolation functions?

One limitation of this method is that the accuracy of the approximation depends on the number of data points used. Additionally, the interpolation function may not accurately represent the true behavior of the function outside of the given data range.

Are there any alternatives to the Lagrange Family of Interpolation functions?

Yes, there are other interpolation methods such as Newton's divided differences and cubic splines. Each method has its own advantages and limitations, and the choice of which method to use depends on the specific problem at hand.

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