Simplifying lagrange interpolation polynomial

In summary, we can simplify the original expression by rewriting it in a different form and then cancelling out terms in the numerator and denominator. This will result in a much simpler expression to work with.
  • #1
lemonthree
51
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Now $\sum_{i=0}^{10}(x_{i}+1) L _{10,i}(5) = (x_{0}+1) L _{10,0}(5) + (x_{1}+1) L _{10,1}(5) + ... + (x_{10}+1) L _{10,10}(5)$

Which I can further decompose into
$\frac{(x_{0}+1)(5-x_{1})(5-x_{2})...(5-x_{10})}{(x_{0}-x_{1})(x_{0}-x_{2})...(x_{0}-x_{10})} + \frac{(x_{1}+1)(5-x_{0})(5-x_{2})...(5-x_{10})}{(x_{1}-x_{0})(x_{1}-x_{2})...(x_{1}-x_{10})} + ... + \frac{(x_{10}+1)(5-x_{0})(5-x_{2})...(5-x_{9})}{(x_{10}-x_{0})(x_{10}-x_{2})...(x_{10}-x_{9})}$

since we know the formula for $L _{10,i}(5)$.

I need help simplifying this expression, because I think there should be a trick somewhere to making this question much easier to solve, but I just don't know it yet. I don't think it would be wise to expand this out as there are 11 $ x_{i}$ terms which would mean 11 x 10 = 110 terms in the denominator.
 
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  • #2


Hello,

I understand your concern about simplifying this expression as it can be quite daunting to deal with a large number of terms. However, there is indeed a trick that can make this expression much easier to solve.

First, let's rewrite the expression in a slightly different form:
$\frac{(x_{0}+1)(x_{1}+1)(x_{2}+1)...(x_{10}+1)(5-x_{0})(5-x_{1})(5-x_{2})...(5-x_{10})}{(x_{0}-x_{1})(x_{0}-x_{2})...(x_{0}-x_{10})(x_{1}-x_{0})(x_{1}-x_{2})...(x_{1}-x_{10})...(x_{10}-x_{0})(x_{10}-x_{2})...(x_{10}-x_{9})}$

Now, let's focus on just one term in the numerator, say $(x_{0}+1)(5-x_{0})$. We can rewrite this as:
$(x_{0}+1)(5-x_{0}) = 5+x_{0}-x_{0}^{2}$.

Notice that the term $x_{0}$ cancels out, leaving us with $5-x_{0}^{2}$.

We can apply this same logic to each term in the numerator, and we will end up with:
$\frac{(5-x_{0}^{2})(5-x_{1}^{2})(5-x_{2}^{2})...(5-x_{10}^{2})}{(x_{0}-x_{1})(x_{0}-x_{2})...(x_{0}-x_{10})(x_{1}-x_{0})(x_{1}-x_{2})...(x_{1}-x_{10})...(x_{10}-x_{0})(x_{10}-x_{2})...(x_{10}-x_{9})}$

Now, we can simplify the expression further by noticing that each term in the denominator has a corresponding term in the numerator with the same exponent. For example, $(x_{0}-x_{1})$ in the denominator has a corresponding term $(5-x_{0}^{2})$ in the numerator. So, we can cancel out these terms, leaving us with:
$\frac{(5
 

What is Lagrange interpolation polynomial?

Lagrange interpolation polynomial is a mathematical technique used to approximate a function using a finite number of data points. It is named after mathematician Joseph-Louis Lagrange and is commonly used in numerical analysis and computer science.

How is Lagrange interpolation polynomial calculated?

To calculate Lagrange interpolation polynomial, first identify a set of data points (x,y) that represent the function you want to approximate. Then, use the Lagrange formula to construct the polynomial. This involves multiplying each data point by a Lagrange basis polynomial and summing them together. The resulting polynomial will pass through all the given data points.

Why is simplifying Lagrange interpolation polynomial important?

Simplifying Lagrange interpolation polynomial can be important for several reasons. Firstly, it can reduce the complexity of the polynomial, making it easier to work with and understand. Additionally, simplifying can help to improve the accuracy of the approximation and reduce the risk of numerical errors.

What are the advantages of using Lagrange interpolation polynomial?

Lagrange interpolation polynomial has several advantages, including its simplicity and ease of use. It also provides a smooth and continuous approximation of a function, making it useful for applications where a high level of accuracy is required. Additionally, it allows for the interpolation of data points that are not evenly spaced.

Are there any limitations to Lagrange interpolation polynomial?

While Lagrange interpolation polynomial is a useful technique, it does have some limitations. It can be computationally expensive for a large number of data points, and the accuracy of the approximation can decrease if the data points are not evenly spaced. It also may not accurately represent the behavior of the function outside of the given data points.

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