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Lagrange's Interpolation formula

In this section, we shall obtain an interpolating polynomial when the given data has unequal tabular points. However, before going to that, we see below an important result.

 

THEOREM 12.3.1   The kth divided difference Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com can be written as: 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Proof. We will prove the result by induction on k The result is trivially true for k = 0 For k = 1

 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 

Let us assume that the result is true for k = n  i.e., 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com =  Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Consider k = n + 1 then the (n + 1)th divided difference is 

 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

which on rearranging the terms gives the desired result. Therefore, by mathematical induction, the proof of the theorem is complete. height6pt width 6pt depth 0pt

Remark 12.3.2   In view of the theorem 12.3.1 the kth divided difference of a function f(x) remains unchanged regardless of how its arguments are interchanged, i.e., it is independent of the order of its arguments.

Now, if a function is approximated by a polynomial of degree n, then , its (n+1)th divided difference relative to x, x,x1,.......,xn will be zero,(Remark 12.2.6) i.e.,

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Using this result, Theorem 12.3.1 gives 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

or, 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B ComLagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com
which gives , 

f(x) = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Note that the expression on the right is a polynomial of degree n and takes the value f (xi) at x = xi for i = 0,1,......, (n-1)

This polynomial approximation is called LAGRANGE'S INTERPOLATION FORMULA.

 

Remark 12.3.3   In view of the Remark (12.2.9), we can observe that Pn (x) is another form of Lagrange's Interpolation polynomial formula as obtained above. Also the remainder term Rn+1 gives an estimate of error between the true value and the interpolated value of the function.

 

Remark 12.3.4   We have seen earlier that the divided differences are independent of the order of its arguments. As the Lagrange's formula has been derived using the divided differences, it is not necessary here to have the tabular points in the increasing order. Thus one can use Lagrange's formula even when the points x, x,x1,.....,xk.......,xn  are in any order, which was not possible in the case of Newton's Difference formulae.

 

Remark 12.3.5   One can also use the Lagrange's Interpolating Formula to compute the value of x for a given value of  y = f(x)This is done by interchanging the roles of x and y, i.e. while using the table of values, we take tabular points as yand nodal points are taken as xk,

 

EXAMPLE 12.3.6   Using the following data, find by Lagrange's formula, the value of f(x) at x = 10

$ i$

0

1

2

3

4

$ x_i$

9.3

9.6

10.2

10.4

10.8

$ y_i = f(x_i)$

11.40

12.80

14.70

17.00

19.80

Also find the value of $ x$ where  f(x) = 16.00
Solution: To compute f(10), we first calculate the following products: 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com =  Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B ComLagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 


Thus, 

f(10) ≈ Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

= 13.197845

 


Now to find the value of $ x$ such that  f (x) = 16 we interchange the roles of $ x$ and y and calculate the following products: 

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B ComLagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com = Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 

Thus,the required value of $ x$ is obtained as: 

x ≈ Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

Lagrange`s Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com

 ≈  10.39123

 

 

EXERCISE 12.3.7   The following table gives the data for steam pressure $ P$ vs temperature $ T$ :

$ T$

360

365

373

383

390

$ P=f(T)$

154.0

165.0

190.0

210.0

240.0

Compute the pressure at $ T=375.$

EXERCISE 12.3.8   Compute from following table the value of y for $ x=6.20$ :

$ x$

5.60

5.90

6.50

6.90

7.20

$ y$

2.30

1.80

1.35

1.95

2.00

Also find the value of $ x$ where  y = 1.00

The document Lagrange's Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics | Business Mathematics and Statistics - B Com is a part of the B Com Course Business Mathematics and Statistics.
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FAQs on Lagrange's Interpolation - Interpolation and Extrapolation, Business Mathematics and Statistics - Business Mathematics and Statistics - B Com

1. What is Lagrange's interpolation method?
Ans. Lagrange's interpolation method is a mathematical technique used to find an unknown value between two known data points. It involves constructing a polynomial function that passes through these points and can be used to estimate the value at any intermediate point within the range.
2. How does Lagrange's interpolation method work?
Ans. Lagrange's interpolation method works by constructing a polynomial function that passes through the given data points. The polynomial is derived using a set of interpolation formulas known as Lagrange polynomials. These polynomials are then combined to form a single polynomial equation, which can be used for interpolation or estimation.
3. What is the difference between interpolation and extrapolation?
Ans. Interpolation refers to estimating values within the range of known data points, while extrapolation involves estimating values outside the range of known data points. In interpolation, the estimated values are based on the assumption that the data follows a certain pattern within the known range, whereas extrapolation extends that assumption beyond the known range, which can be less reliable.
4. How can Lagrange's interpolation method be applied in business mathematics?
Ans. Lagrange's interpolation method can be applied in business mathematics for various purposes. It can be used to estimate sales figures, demand for a product, or market trends based on historical data points. Additionally, it can be utilized in financial modeling to forecast future values, such as stock prices or interest rates, based on past observations.
5. What are the limitations of Lagrange's interpolation method?
Ans. Lagrange's interpolation method has certain limitations. Firstly, it assumes a smooth and continuous relationship between the known data points, which may not always hold true in real-world scenarios. Secondly, the accuracy of the estimation heavily depends on the number and distribution of the data points. Insufficient or unevenly spaced data points can lead to significant errors in the interpolated values. Lastly, extrapolation using Lagrange's method can be highly unreliable, as it extends the assumed pattern beyond the known range.
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