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Polynomial Approximation and Interpolation

الكلية كلية العلوم للبنات     القسم قسم فيزياء الليزر     المرحلة 3
أستاذ المادة نزار سالم شنان الزبيدي       4/27/2011 7:14:24 AM

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 1 lecture 3

4.6 DIFFERENCE TABLES AND DIFFERENCE POLYNOMIALS

Fitting approximating polynomials to tabular data is considerably simpler when the

values of the independent variable are equally spaced. Implementation of polynomial

fitting for equally spaced data is best accomplished in terms of differences. Consequently,

the concept of differences, difference tables, and difference polynomials are introduced in

this section.

4.6.1. Difference Tables

A difference table is an arrangement of a set of data, [x,f(x)], in a table with the x values

in monotonic ascending order, with additional columns composed of the differences of

the numbers in the preceding column. A triangular array is obtained, as illustrated in

Table 4.6.

The numbers appearing in a difference table are unique. However, three different

interpretations can be assigned to these numbers, each with its unique notation. The

forward difference relative to point i is (fi+1 - fi), the backward difference relative to point

i+ 1 is (fi+1 - fi), and the centered difference relative to point i+l/2 is (fi+1 - fi). The forward

difference operator is defined as

A difference table, such as Table 4.6, can be interpreted as a forward-difference table,

a backward-difference table, or a centered-difference table, as illustrated in Figure 4.7.

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 2 lecture 3

The numbers in the tables are identical. Only the notation is different. The three

different types of interpretation and notation simplify the use of difference tables in the

construction of approximating polynomials, which is discussed in Sections 4.6.2 to 4.6.4.

Example 4.7. Difference table.

Let s construct a six-place difference table for the function f(x) = 1/x for 3.1 x 3.9

with x = 0.1. The results are presented in Table 4.7, which uses the forward-difference

notation to denote the columns of differences.

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 3 lecture 3

4.6.2, The Newton Forward-Difference Polynomial

Given n + 1 data points, [x,f(x)], one form of the unique nth-degree polynomial that

passes through the n + 1 points is given by

Equation (4.88) does not look anything like the direct fit polynomial [see Eq. (4.34)], the

Lagrange polynomial [see Eq. (4.46)], or the divided difference polynomial [see Eq.

(4.65)]. However, if Eq. (4.88) is a polynomial of degree n and passes exactly through the

n + 1 data points, it must be one form of the unique polynomial that passes through this

set of data.

The interpolating variable, s = (x - xo)/h, is linear in x. Consequently, the last term

in Eq. (4.88) is order n, and Eq. (4.88) is an nth-degree polynomial. Let s = 0. Then x=xo,

f =fo, and Pn(xo) =fo. Let s = 1.

Then x = x0 + h = x1, f =fl , and Pn(x1) = fo+ fo=fo + (f1-fo) =f1. In a similar manner, it

can be shown that Pn(x) =f(x) for the n + 1 discrete points. Therefore, Pn(x) is the desired

unique nth-degree polynomial. Equation (4.88) is called the Newton forward-difference

polynomial.

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 4 lecture 3

A major advantage of the Newton forward-difference polynomial, in addition to its

simplicity, is that each higher-degree polynomial is obtained from the previous lowerdegree

polynomial simply by adding the next term. The work already performed for the

lower-degree polynomial does not have to be repeated. This feature is in sharp contrast to

the direct fit polynomial and the Lagrange polynomial, where all of the work must be

repeated each time the degree of the polynomial is changed. This feature makes it simple

to determine when the desired accuracy has been obtained. When the next term in the

polynomial is less than some prespecified value, the desired accuracy has been obtained.

Example 4.8. Newton forward-difference polynomial.

From the six-place difference table for f(x) = l/x, Table 4.7, calculate P(3.44) by the

Newton forward-difference polynomial. The exact solution is f(3.44) = 1/3.44 = 0.290698

... In Table 4.7, h = 0.1. Choose x0 = 3.40. Then,

Evaluating Eq. (4.94) term by term yields the following results and errors:

P(3.44) = 0.290756 linear interpolation ErrorC.44) = 0.000058

= 0.290700 quadratic interpolation = 0.000002

= 0.290698 cubic interpolation = 0.000000

The advantage of higher-degree interpolation is obvious.

In this example, the base point, x0 = 3.4, was selected so that the point of

interpolation, x = 3.44, falls within the range of data used to determine the polynomial,

that is, interpolation occurs. If x0 is chosen so that x does not fall within the range of fit,

extrapolation occurs, and the results are less accurate. For example, let x0 = 3.2, for which

s = 2.4. The following results and errors are obtained:

(3.44) = 0.289772 linear extrapolation Error = -0.000926

= 0.290709 quadratic extrapolation = 0.000011

= 0.290698 cubic interpolation = 0.000000

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 5 lecture 3

The increase in error is significant for linear and quadratic extrapolation. For x0 = 3.2, the

cubic yields an interpolating polynomial.

4.6.3 The Newton Backward-Difference Polynomial

The Newton forward-difference polynomial, Eq. (4.88), can be applied at the top or in the

middle of a set of tabular data, where the downward-sloping forward differences

illustrated in Figure 4.7a exist. However, at the bottom of a set of tabular data, the

required forward differences do not exist, and the Newton forward-difference polynomial

cannot be used. In that case, an approach that uses the upward-sloping backward

differences illustrated in Figure 4.7b is required. Such a polynomial is developed in this

section.

Given n + 1 data points, [x,f(x)], one form of the unique nth-degree polynomial that

passes through the n + 1 points is given by

Polynomial Approximation and Interpolation Chapter 4

Nizar Salim 6 lecture 3

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