Interpolation and Finite Difference Operators, Slides of Mathematical Methods for Numerical Analysis and Optimization

An in-depth exploration of interpolation methods, including newton's forward difference, newton's backward difference, and lagrange's interpolation formula. The text also covers divided differences and interpolation in two dimensions, as well as cubic spline interpolation. Finite difference operators, such as forward differences, backward differences, and central differences, are introduced and explained in detail.

Typology: Slides

2011/2012

Uploaded on 08/05/2012

saruy
saruy 🇮🇳

4.5

(120)

130 documents

1 / 42

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
docsity.com
pf3
pf4
pf5
pf8
pf9
pfa
pfd
pfe
pff
pf12
pf13
pf14
pf15
pf16
pf17
pf18
pf19
pf1a
pf1b
pf1c
pf1d
pf1e
pf1f
pf20
pf21
pf22
pf23
pf24
pf25
pf26
pf27
pf28
pf29
pf2a

Partial preview of the text

Download Interpolation and Finite Difference Operators and more Slides Mathematical Methods for Numerical Analysis and Optimization in PDF only on Docsity!

Chapter 5

Interpolation

Chapter 5

Interpolation

IntroductionIntroduction

Finite differences play anFinite differences play animportant role in numericalimportant role in numericaltechniques, wheretechniques, wheretabulated values of thetabulated values of thefunctions are available.functions are available.For instance, consider aFor instance, consider afunctionfunction

y^

f^

x

That is, for given a table ofvalues,^ (^

,^

),^

k^

k

x^

y^

k^

n

^

the process of estimatingthe value of

y , for any

intermediate value of

x , is

called interpolation.

The method of computingthe value of

y , for a given

value of

x , lying outside

the table of values of

x^

is

known as extrapolation.

To be explicit, we write

0

1

0

1

2

1

1

1

n^

n^

n

y^

y^

y

y^

y^

y

y^

y^

y

^

^

^

^

^

^

^

^

^

Similarly, the differences ofthe first differences arecalled second differences,defined by

2

2

0

1

0

1

2

1

,

y^

y^

y^

y^

y^

y

^

 

 

^

 

 

Thus, in general

2

1

i^

i^

i

y^

y^

y 

^

 

 

docsity.com

BackwardBackwardDifferencesDifferences

1

,^ (

1),

,

i^

i^

i

y^

y^

y^

i^

n^

n

^

^

^

^

^

1

1

0

2

2

1 1

n^

n^

n

y^

y^

y

y^

y^

y

y^

y^

y^ 

^

^

^

^

^

^

^

^

OR

docsity.com

Thus, in general, the secondbackward differences are^2

,^1

, (^

1),..., 2

i^

i^

i

y^

y^

y^

i^

n^ n 

^

 

 

^

while the

k-th

backward

differences are given as

1

1 1

,^

, (^

1),...,

k^

k^

k

i^

i^

i

y^

y^

y^

i^ n

n^

k

^

 

^

 

 ^

^

1 2^

1

0

3 2^

2

1

,^

,

y^

y^

y^

y^

y^

y

^

^

^

In general

(1 2)

(1 2)

i^

i^

i

y^

y^

y

^

^

^

Higher order differences aredefined as follows:

2

(1 2)

(1 2)

i^

i^

i

y^

y^

y

^

 ^

^

 1

1

(1 2)

(1 2)

n^

n^

n

i^

i^

i

y^

y^

y

^

^

^

^

Shift operator, E Let

y^ =

f^ ( x

) be a function of

x ,

and let

x^ takes the consecutive values

x ,^

x + h

,^ x^

+ 2

h , etc. We

then define an operator havingthe property

( )

(^

)

E f

x

f^

x^

h

^

Thus, when

E^

operates on

f^ (

x ),

the result is the next value ofthe function. Here,

E^

is called

the shift operator. If we applythe operator

E^

twice on

f^ (

x ),

we get

2

( )

[^

( )]

[^

(^

)]^

(^

2 )

E^

f^ x

E E f

x

E f

x

h

f^ x

h

^

^

^