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Chapter 18 1
Interpolation
Chapter 18
• Estimation of intermediate values between precise data points. The most common method is:
• Although there is one and only one nth-order polynomial that fits n+1 points, there are a variety of mathematical formats in which this polynomial can be expressed:
– The Newton polynomial
– The Lagrange polynomial
n
nxaxaxaaxf ++++= L2
210)(
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Chapter 18 2
Figure 18.1
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Chapter 18 3
Figure
18.2
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Chapter 18 4
Newton’s Divided-Difference
Interpolating Polynomials
Linear Interpolation/
• Is the simplest form of interpolation, connecting two data
points with a straight line.
• f1(x) designates that this is a first-order interpolating
polynomial.
)()()(
)()(
)()()()(
0
01
0101
01
01
0
01
xxxx
xfxfxfxf
xx
xfxf
xx
xfxf
−−
−+=
−
−=
−
−
Linear-interpolation
formula
Slope and a
finite divided
difference
approximation to
1st derivative
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Chapter 18 5
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Chapter 18 6
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7
Quadratic Interpolation/
• If three data points are available, the estimate is
improved by introducing some curvature into the line
connecting the points.
• A simple procedure can be used to determine the
values of the coefficients.
))(()()( 1020102 xxxxbxxbbxf −−+−+=
02
01
01
12
12
22
01
0111
000
)()()()(
)()(
)(
xx
xx
xfxf
xx
xfxf
bxx
xx
xfxfbxx
xfbxx
−
−
−−
−
−
==
−
−==
==
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Chapter 18 8
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by Lale Yurttas, Texas
A&M University
Chapter 18 9
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Chapter 18 10
General Form of Newton’s Interpolating Polynomials/
0
02111011
011
0122
011
00
01110
012100100
],,,[],,,[],,,,[
],[],[],,[
)()(],[
],,,,[
],,[
],[
)(
],,,[)())((
],,[))((],[)()()(
xx
xxxfxxxfxxxxf
xx
xxfxxfxxxf
xx
xfxfxxf
xxxxfb
xxxfb
xxfb
xfb
xxxfxxxxxx
xxxfxxxxxxfxxxfxf
n
nnnnnn
ki
kjji
kji
ji
ji
ji
nnn
nnn
n
−
−=
−
−=
−
−=
=
=
=
=
−−−++
−−+−+=
−−−−
−
−−
KKK
M
L
M
LLL
Bracketed function
evaluations are finite
divided differences
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Chapter 18 11
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Chapter 18 12
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Chapter 18 13
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Chapter 18 14
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Chapter 18 15
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Chapter 18 16
Errors of Newton’s Interpolating Polynomials/
• Structure of interpolating polynomials is similar to the Taylor
series expansion in the sense that finite divided differences are
added sequentially to capture the higher order derivatives.
• For an nth-order interpolating polynomial, an analogous
relationship for the error is:
• For non differentiable functions, if an additional point f(xn+1)
is available, an alternative formula can be used that does not
require prior knowledge of the function:
)())(()!1(
)(10
)1(
n
n
n xxxxxxn
fR −−−
+=
+
Lξ
)())(](,,,,[ 10011 nnnnn xxxxxxxxxxfR −−−≅ −+ LK
ξ Is somewhere
containing the unknown
and he data
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Chapter 18 17
Lagrange Interpolating Polynomials
• The Lagrange interpolating polynomial is simply a
reformulation of the Newton’s polynomial that
avoids the computation of divided differences:
∏
∑
≠=
=
−
−=
=
n
ijj ji
j
i
n
i
iin
xx
xxxL
xfxLxf
0
0
)(
)()()(
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Chapter 18 18
( )( )( )( )
( )( )( )( )
( )( )( )( )
)(
)()()(
)()()(
2
1202
10
1
2101
200
2010
212
1
01
00
10
11
xfxxxx
xxxx
xfxxxx
xxxxxf
xxxx
xxxxxf
xfxx
xxxf
xx
xxxf
−−
−−+
−−
−−+
−−
−−=
−
−+
−
−=
•As with Newton’s method, the Lagrange version has an
estimated error of:
∏=
− −=n
i
innn xxxxxxfR0
01 )(],,,,[ K
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Chapter 18 19
Figure 18.10
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Chapter 18 20
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Chapter 18 21
)()()(
)()(
)()()()(
0
01
0101
01
01
0
01
xxxx
xfxfxfxf
xx
xfxf
xx
xfxf
−−
−+=
−
−=
−
−
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Chapter 18 22
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Chapter 18 23
Coefficients of an Interpolating
Polynomial
• Although both the Newton and Lagrange
polynomials are well suited for determining
intermediate values between points, they do not
provide a polynomial in conventional form:
• Since n+1 data points are required to determine n+1
coefficients, simultaneous linear systems of equations
can be used to calculate “a”s.
n
xxaxaxaaxf ++++= L2
210)(
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Chapter 18 24
n
nnnnn
n
n
n
n
xaxaxaaxf
xaxaxaaxf
xaxaxaaxf
+++=
+++=
+++=
L
M
L
L
2
210
1
2
121101
0
2
020100
)(
)(
)(
Where “x”s are the knowns and “a”s are the unknowns.
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Chapter 18 25
Figure 18.13
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Chapter 18 26
Spline Interpolation
• There are cases where polynomials can lead to
erroneous results because of round off error
and overshoot.
• Alternative approach is to apply lower-order
polynomials to subsets of data points. Such
connecting polynomials are called spline
functions.
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Chapter 18 27
Figure 18.14
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Chapter 18 28
Figure 18.15
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Chapter 18 29
Figure 18.16
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Chapter 18 30
Figure 18.17
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Chapter 18 31
Quadratic Splines
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Chapter 18 32
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Chapter 18 33
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Chapter 18 34
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Chapter 18 35
Cubic Splines
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Chapter 18 36
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Chapter 18 37
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Chapter 18 38
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Chapter 18 39
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Chapter 18 40
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Chapter 18 41
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Chapter 18 42