Newton's interpolating polynomial

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In summary, the problem involves interpolating the function ln(x+1) using a Newton's polynomial of degree 2 with points x_0=0, x_1=0.6, and x_2=0.9. Divided differences are used to calculate the coefficients of the polynomial, resulting in c_0 = 0, c_1 \approx 0.78, and c_2 \approx -0.68. However, upon plotting the polynomial, it does not fit as expected. After recalculating the coefficients, it is discovered that the data is spaced unequally, resulting in a different value for c_2. After redoing the algebra, the correct value for c_2 is
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fluidistic
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Homework Statement


Interpolate the function ln(x+1) using a Newton's polynomial of degree 2. The points to be interpolated are [itex]x_0=0[/itex], [itex]x_1=0.6[/itex] and [itex]x_2=0.9[/itex].


Homework Equations



[itex]p_2(x)=c_0+c_1(x-x_0)+c_2(x-x_0)(x-x_1)[/itex].

The Attempt at a Solution


So I used divided differences in order to calculate the coefficients of the polynomial.
[itex]f[x_1, x_2]=\frac{f(x_2)-f(x_1)}{x_2-x_1} \approx 0.1718502569[/itex].
[itex]f[x_0,x_1]\approx 0.7833393821[/itex].
[itex]f[x_0,x_1,x_2]=\approx -0.6794[/itex].
This gives [itex]c_0=0, c_1\approx 0.78[/itex] and [itex]c_2 \approx -0.68[/itex].
I've plotted both ln(x+1) and the polynomial and... it doesn't fit as it should. In fact it interpolates ln(x+1) in 0 and 0.6 but not in 0.9 as it should.
Where did I go wrong?! I've redone the algebra twice, even with a calculator and I'm still getting this wrong result.
Thanks for any help.
 

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  • #2
fludistic - I get a different value for c2 of approx -.2344... Don't forget, your data is spaced unequally.
 
  • #3
TheoMcCloskey said:
fludistic - I get a different value for c2 of approx -.2344... Don't forget, your data is spaced unequally.

Thanks. I've redone all from scratch and now get your result. :)
 

Related to Newton's interpolating polynomial

1. What is Newton's interpolating polynomial?

Newton's interpolating polynomial is a mathematical method used to approximate a function or data set using a series of data points. It is named after Sir Isaac Newton, who first developed the method in the 17th century.

2. How is Newton's interpolating polynomial calculated?

To calculate Newton's interpolating polynomial, one must first determine the coefficients of the polynomial using a divided differences table. This table is created by taking the differences between consecutive data points and using them to calculate the coefficients. The final polynomial is then constructed using these coefficients.

3. What is the difference between Newton's interpolating polynomial and Lagrange's interpolating polynomial?

Both Newton's and Lagrange's interpolating polynomials are used to approximate a function or data set, but they use different methods to do so. Newton's method involves constructing a polynomial using divided differences, while Lagrange's method uses a series of basis polynomials to create the final polynomial.

4. What are the applications of Newton's interpolating polynomial?

Newton's interpolating polynomial has several applications in mathematics and engineering. It is commonly used in numerical analysis, curve fitting, and data interpolation. It is also used in fields such as computer graphics and signal processing.

5. What are the limitations of Newton's interpolating polynomial?

One limitation of Newton's interpolating polynomial is that it can only interpolate data points within the range of the given data set. It also requires evenly spaced data points, and can produce large errors if the data is not well-behaved. Additionally, as the degree of the polynomial increases, the accuracy of the approximation may decrease.

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