Numerical methods by Jeffrey R. Chasnov

Introduction to Numerical
Methods
Lecture notes for MATH 3311
Jeffrey R. Chasnov
The Hong Kong University of
Science and Technology

The Hong Kong University of Science and Technology
Department of Mathematics
Clear Water Bay, Kowloon
Hong Kong
Copyright ○c 2012 by Jeffrey Robert Chasnov
This work is licensed under the Creative Commons Attribution 3.0 Hong Kong License.
To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/hk/
or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco,
California, 94105, USA.

Preface
What follows are my lecture notes for Math 3311: Introduction to Numerical
Methods, taught at the Hong Kong University of Science and Technology. Math
3311, with two lecture hours per week, is primarily for non-mathematics majors
and is required by several engineering departments.
All web surfers are welcome to download these notes at
http://www.math.ust.hk/~machas/numerical-methods.pdf
and to use the notes freely for teaching and learning. I welcome any comments,
suggestions or corrections sent by email to jeffrey.chasnov@ust.hk.
iii

Contents
1 IEEE Arithmetic 1
1.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Numbers with a decimal or binary point . . . . . . . . . . . . . . 1
1.3 Examples of binary numbers . . . . . . . . . . . . . . . . . . . . . 1
1.4 Hex numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.5 4-bit unsigned integers as hex numbers . . . . . . . . . . . . . . . 2
1.6 IEEE single precision format: . . . . . . . . . . . . . . . . . . . . 2
1.7 Special numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.8 Examples of computer numbers . . . . . . . . . . . . . . . . . . . 3
1.9 Inexact numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.9.1 Find smallest positive integer that is not exact in single
precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.10 Machine epsilon . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.11 IEEE double precision format . . . . . . . . . . . . . . . . . . . . 5
1.12 Roundoff error example . . . . . . . . . . . . . . . . . . . . . . . 6
2 Root Finding 7
2.1 Bisection Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Newton’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Secant Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.1 Estimate
√
2 = 1.41421356 using Newton’s Method . . . . 8
2.3.2 Example of fractals using Newton’s Method . . . . . . . . 8
2.4 Order of convergence . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4.1 Newton’s Method . . . . . . . . . . . . . . . . . . . . . . . 9
2.4.2 Secant Method . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Systems of equations 13
3.1 Gaussian Elimination . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 퐿푈 decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Partial pivoting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Operation counts . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.5 System of nonlinear equations . . . . . . . . . . . . . . . . . . . . 21
4 Least-squares approximation 23
4.1 Fitting a straight line . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Fitting to a linear combination of functions . . . . . . . . . . . . 24
v

vi CONTENTS
5 Interpolation 27
5.1 Polynomial interpolation . . . . . . . . . . . . . . . . . . . . . . . 27
5.1.1 Vandermonde polynomial . . . . . . . . . . . . . . . . . . 27
5.1.2 Lagrange polynomial . . . . . . . . . . . . . . . . . . . . . 28
5.1.3 Newton polynomial . . . . . . . . . . . . . . . . . . . . . . 28
5.2 Piecewise linear interpolation . . . . . . . . . . . . . . . . . . . . 29
5.3 Cubic spline interpolation . . . . . . . . . . . . . . . . . . . . . . 30
5.4 Multidimensional interpolation . . . . . . . . . . . . . . . . . . . 33
6 Integration 35
6.1 Elementary formulas . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1.1 Midpoint rule . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1.2 Trapezoidal rule . . . . . . . . . . . . . . . . . . . . . . . 36
6.1.3 Simpson’s rule . . . . . . . . . . . . . . . . . . . . . . . . 37
6.2 Composite rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.2.1 Trapezoidal rule . . . . . . . . . . . . . . . . . . . . . . . 37
6.2.2 Simpson’s rule . . . . . . . . . . . . . . . . . . . . . . . . 38
6.3 Local versus global error . . . . . . . . . . . . . . . . . . . . . . . 38
6.4 Adaptive integration . . . . . . . . . . . . . . . . . . . . . . . . . 39
7 Ordinary differential equations 43
7.1 Examples of analytical solutions . . . . . . . . . . . . . . . . . . 43
7.1.1 Initial value problem . . . . . . . . . . . . . . . . . . . . . 43
7.1.2 Boundary value problems . . . . . . . . . . . . . . . . . . 44
7.1.3 Eigenvalue problem . . . . . . . . . . . . . . . . . . . . . 45
7.2 Numerical methods: initial value problem . . . . . . . . . . . . . 46
7.2.1 Euler method . . . . . . . . . . . . . . . . . . . . . . . . . 46
7.2.2 Modified Euler method . . . . . . . . . . . . . . . . . . . 46
7.2.3 Second-order Runge-Kutta methods . . . . . . . . . . . . 47
7.2.4 Higher-order Runge-Kutta methods . . . . . . . . . . . . 48
7.2.5 Adaptive Runge-Kutta Methods . . . . . . . . . . . . . . 49
7.2.6 System of differential equations . . . . . . . . . . . . . . . 50
7.3 Numerical methods: boundary value problem . . . . . . . . . . . 51
7.3.1 Finite difference method . . . . . . . . . . . . . . . . . . . 51
7.3.2 Shooting method . . . . . . . . . . . . . . . . . . . . . . . 53
7.4 Numerical methods: eigenvalue problem . . . . . . . . . . . . . . 54
7.4.1 Finite difference method . . . . . . . . . . . . . . . . . . . 54
7.4.2 Shooting method . . . . . . . . . . . . . . . . . . . . . . . 56

Chapter 1
IEEE Arithmetic
1.1 Definitions
Bit = 0 or 1
Byte = 8 bits
Word = Reals: 4 bytes (single precision)
8 bytes (double precision)
= Integers: 1, 2, 4, or 8 byte signed
1, 2, 4, or 8 byte unsigned
1.2 Numbers with a decimal or binary point
· Decimal: 103 102 101 100 10−1 10−2 10−3 10−4
Binary: 23 22 21 20 2−1 2−2 2−3 2−4
1.3 Examples of binary numbers
Decimal Binary
1 1
2 10
3 11
4 100
0.5 0.1
1.5 1.1
1.4 Hex numbers
{0, 1, 2, 3, . . . , 9, 10, 11, 12, 13, 14, 15} = {0, 1, 2, 3.......9, a,b,c,d,e,f}
1

2 CHAPTER 1. IEEE ARITHMETIC
1.5 4-bit unsigned integers as hex numbers
Decimal Binary Hex
1 0001 1
2 0010 2
3 0011 3
...
...
...
10 1010 a
...
...
...
15 1111 f
1.6 IEEE single precision format:
⏞ 푠⏟
0
⏞ 푒⏟
1
2
3
4
5
6
78
⏞ 푓⏟
9
· · · · · · · ·31
# = (−1)푠 × 2푒−127 × 1.f
where s = sign
e = biased exponent
p=e-127 = exponent
1.f = significand (use binary point)

1.7. SPECIAL NUMBERS 3
1.7 Special numbers
Smallest exponent: e = 0000 0000, represents denormal numbers (1.f → 0.f)
Largest exponent: e = 1111 1111, represents ±∞, if f = 0
e = 1111 1111, represents NaN, if f̸= 0
Number Range: e = 1111 1111 = 28 - 1 = 255 reserved
e = 0000 0000 = 0 reserved
so, p = e - 127 is
1 - 127 ≤ p ≤ 254-127
-126 ≤ p ≤ 127
Smallest positive normal number
= 1.0000 0000 · · · · ·· 0000× 2−126
≃ 1.2 × 10−38
bin: 0000 0000 1000 0000 0000 0000 0000 0000
hex: 00800000
MATLAB: realmin(’single’)
Largest positive number
= 1.1111 1111 · · · · ·· 1111× 2127
= (1 + (1 − 2−23)) × 2127
≃ 2128 ≃ 3.4 × 1038
bin: 0111 1111 0111 1111 1111 1111 1111 1111
hex: 7f7fffff
MATLAB: realmax(’single’)
Zero
bin: 0000 0000 0000 0000 0000 0000 0000 0000
hex: 00000000
Subnormal numbers
Allow 1.f → 0.f (in software)
Smallest positive number = 0.0000 0000 · · · · · 0001 × 2−126
= 2−23 × 2−126 ≃ 1.4 × 10−45
1.8 Examples of computer numbers
What is 1.0, 2.0 1/2 in hex ?
1.0 = (−1)0 × 2(127−127) × 1.0
Therefore, 푠 = 0, 푒 = 0111 1111, 푓 = 0000 0000 0000 0000 0000 000
bin: 0011 1111 1000 0000 0000 0000 0000 0000
hex: 3f80 0000
2.0 = (−1)0 × 2(128−127) × 1.0
Therefore, 푠 = 0, 푒 = 1000 0000, 푓 = 0000 0000 0000 0000 0000 000
bin: 0100 00000 1000 0000 0000 0000 0000 0000
hex: 4000 0000

1/2 = (−1)0 × 2(126−127) × 1.0
Therefore, 푠 = 0, 푒 = 0111 1110, 푓 = 0000 0000 0000 0000 0000 000
bin: 0011 1111 0000 0000 0000 0000 0000 0000
hex: 3f00 0000
1.9 Inexact numbers
Example:
1
3
= (−1)0 ×
1
4
× (1 +
1
3
),
so that 푝 = 푒 − 127 = −2 and 푒 = 125 = 128 − 3, or in binary, 푒 = 0111 1101.
How is 푓 = 1/3 represented in binary? To compute binary number, multiply
successively by 2 as follows:
0.333 . . . 0.
0.666 . . . 0.0
1.333 . . . 0.01
0.666 . . . 0.010
1.333 . . . 0.0101
etc.
so that 1/3 exactly in binary is 0.010101 . . . . With only 23 bits to represent 푓,
the number is inexact and we have
푓 = 01010101010101010101011,
where we have rounded to the nearest binary number (here, rounded up). The
machine number 1/3 is then represented as
00111110101010101010101010101011
or in hex
3푒푎푎푎푎푎푏.
1.9.1 Find smallest positive integer that is not exact in
single precision
Let 푁 be the smallest positive integer that is not exact. Now, I claim that
푁 − 2 = 223 × 1.11 . . . 1,
and
푁 − 1 = 224 × 1.00 . . . 0.
The integer 푁 would then require a one-bit in the 2−24 position, which is not
available. Therefore, the smallest positive integer that is not exact is 224 + 1 =
16 777 217. In MATLAB, single(224) has the same value as single(224+1). Since
single(224+1) is exactly halfway between the two consecutive machine numbers
224 and 224+2, MATLAB rounds to the number with a final zero-bit in f, which
is 224.

1.10. MACHINE EPSILON 5
1.10 Machine epsilon
Machine epsilon (휖mach) is the distance between 1 and the next largest number.
If 0 ≤ 훿 휖mach/2, then 1 + 훿 = 1 in computer math. Also since
푥 + 푦 = 푥(1 + 푦/푥),
if 0 ≤ 푦/푥 휖mach/2, then 푥 + 푦 = 푥 in computer math.
Find 휖mach
The number 1 in the IEEE format is written as
1 = 20 × 1.000 . . . 0,
with 23 0’s following the binary point. The number just larger than 1 has a 1
in the 23rd position after the decimal point. Therefore,
휖mach = 2−23 ≈ 1.192 × 10−7.
What is the distance between 1 and the number just smaller than 1? Here,
the number just smaller than one can be written as
2−1 × 1.111 . . . 1 = 2−1(1 + (1 − 2−23)) = 1 − 2−24
Therefore, this distance is 2−24 = 휖mach/2.
The spacing between numbers is uniform between powers of 2, with logarith-mic
spacing of the powers of 2. That is, the spacing of numbers between 1 and
2 is 2−23, between 2 and 4 is 2−22, between 4 and 8 is 2−21, etc. This spacing
changes for denormal numbers, where the spacing is uniform all the way down
to zero.
Find the machine number just greater than 5
A rough estimate would be 5(1+휖mach) = 5+5휖mach, but this is not exact. The
exact answer can be found by writing
5 = 22(1 +
1
4
),
so that the next largest number is
22(1 +
1
4
+ 2−23) = 5 + 2−21 = 5 + 4휖mach.
1.11 IEEE double precision format
Most computations take place in double precision, where round-off error is re-duced,
and all of the above calculations in single precision can be repeated for
double precision. The format is
⏞ 푠⏟
0
⏞ 푒⏟
12
3
4
5
6
7
8
9
1011
⏞ 푓⏟
12
· · · · · · · ·63

# = (−1)푠 × 2푒−1023 × 1.f
where s = sign
e = biased exponent
p=e-1023 = exponent
1.f = significand (use binary point)
1.12 Roundoff error example
Consider solving the quadratic equation
푥2 + 2푏푥 − 1 = 0,
where 푏 is a parameter. The quadratic formula yields the two solutions
푥± = −푏 ±
√︀
푏2 + 1.
Consider the solution with 푏 0 and 푥 0 (the 푥+ solution) given by
푥 = −푏 +
√︀
푏2 + 1. (1.1)
As 푏 → ∞,
푥 = −푏 +
√︀
푏2 + 1
√︀
= −푏 + 푏
1 + 1/푏2
√︀
= 푏(
1 + 1/푏2 − 1)
≈ 푏
(︂
1 +
1
2푏2
− 1
)︂
=
1
2푏
.
Now in double precision, realmin ≈ 2.2 × 10−308 and we would like 푥 to be
accurate to this value before it goes to 0 via denormal numbers. Therefore,
푥 should be computed accurately to 푏 ≈ 1/(2 × realmin) ≈ 2 × 10307. What
happens if we compute (1.1) directly? Then 2 √
푥 = 0 when 푏+ 1 = 푏2, or
1 + 1/푏2 = 1. That is 1/푏2 = 휖mach/2, or 푏 =
2/
√
휖mach ≈ 108.
For a subroutine written to compute the solution of a quadratic for a general
user, this is not good enough. The way for a software designer to solve this
problem is to compute the solution for 푥 as
푥 =
1
푏 +
√
푏2 + 1
.
In this form, if 푏2 +1 = 푏2, then 푥 = 1/2푏 which is the correct asymptotic form.

Chapter 2
Root Finding
Solve 푓(푥) = 0 for 푥, when an explicit analytical solution is impossible.
2.1 Bisection Method
The bisection method is the easiest to numerically implement and almost always
works. The main disadvantage is that convergence is slow. If the bisection
method results in a computer program that runs too slow, then other faster
methods may be chosen; otherwise it is a good choice of method.
We want to construct a sequence 푥0, 푥1, 푥2, . . . that converges to the root
푥 = 푟 that solves 푓(푥) = 0. We choose 푥0 and 푥1 such that 푥0 푟 푥1. We
say that 푥0 and 푥1 bracket the root. With 푓(푟) = 0, we want 푓(푥0) and 푓(푥1)
to be of opposite sign, so that 푓(푥0)푓(푥1) 0. We then assign 푥2 to be the
midpoint of 푥0 and 푥1, that is 푥2 = (푥0 + 푥1)/2, or
푥2 = 푥0 +
푥1 − 푥0
2
.
The sign of 푓(푥2) can then be determined. The value of 푥3 is then chosen as
either the midpoint of 푥0 and 푥2 or as the midpoint of 푥2 and 푥1, depending on
whether 푥0 and 푥2 bracket the root, or 푥2 and 푥1 bracket the root. The root,
therefore, stays bracketed at all times. The algorithm proceeds in this fashion
and is typically stopped when the increment to the left side of the bracket
(above, given by (푥1 − 푥0)/2) is smaller than some required precision.
2.2 Newton’s Method
This is the fastest method, but requires analytical computation of the derivative
of 푓(푥). Also, the method may not always converge to the desired root.
We can derive Newton’s Method graphically, or by a Taylor series. We again
want to construct a sequence 푥0, 푥1, 푥2, . . . that converges to the root 푥 = 푟.
Consider the 푥푛+1 member of this sequence, and Taylor series expand 푓(푥푛+1)
about the point 푥푛. We have
푓(푥푛+1) = 푓(푥푛) + (푥푛+1 − 푥푛)푓′(푥푛) + . . . .
7

8 CHAPTER 2. ROOT FINDING
To determine 푥푛+1, we drop the higher-order terms in the Taylor series, and
assume 푓(푥푛+1) = 0. Solving for 푥푛+1, we have
푥푛+1 = 푥푛 −
푓(푥푛)
푓′(푥푛)
.
Starting Newton’s Method requires a guess for 푥0, hopefully close to the root
푥 = 푟.
2.3 Secant Method
The Secant Method is second best to Newton’s Method, and is used when a
faster convergence than Bisection is desired, but it is too difficult or impossible
to take an analytical derivative of the function 푓(푥). We write in place of 푓′(푥푛),
푓′(푥푛) ≈
푓(푥푛) − 푓(푥푛−1)
푥푛 − 푥푛−1
.
Starting the Secant Method requires a guess for both 푥0 and 푥1.
2.3.1 Estimate
√
2 = 1.41421356 using Newton’s Method
The
√
2 is the zero of the function 푓(푥) = 푥2 − 2. To implement Newton’s
Method, we use 푓′(푥) = 2푥. Therefore, Newton’s Method is the iteration
푥푛+1 = 푥푛 −
푥2
푛 − 2
2푥푛
.
We take as our initial guess 푥0 = 1. Then
푥1 = 1 −
−1
2
=
3
2
= 1.5,
푥2 =
3
2
−
9
4 − 2
3
=
17
12
= 1.416667,
푥3 =
17
12
−
172
122 − 2
17
6
=
577
408
= 1.41426.
2.3.2 Example of fractals using Newton’s Method
Consider the complex roots of the equation 푓(푧) = 0, where
푓(푧) = 푧3 − 1.
These roots are the three cubic roots of unity. With
푒푖2휋푛 = 1, 푛 = 0, 1, 2, . . .
the three unique cubic roots of unity are given by
1, 푒푖2휋/3, 푒푖4휋/3.
With
푒푖휃 = cos 휃 + 푖 sin 휃,

2.4. ORDER OF CONVERGENCE 9
and cos (2휋/3) = −1/2, sin (2휋/3) =
√
3/2, the three cubic roots of unity are
푟1 = 1, 푟2 = −
1
2
+
√
3
2
푖, 푟3 = −
1
2
−
√
3
2
푖.
The interesting idea here is to determine which initial values of 푧0 in the complex
plane converge to which of the three cubic roots of unity.
Newton’s method is
푧푛+1 = 푧푛 −
푧3푛
− 1
3푧2푛
.
If the iteration converges to 푟1, we color 푧0 red; 푟2, blue; 푟3, green. The result
will be shown in lecture.
2.4 Order of convergence
Let 푟 be the root and 푥푛 be the 푛th approximation to the root. Define the error
as
휖푛 = 푟 − 푥푛.
If for large 푛 we have the approximate relationship
|휖푛+1| = 푘|휖푛|푝,
with 푘 a positive constant, then we say the root-finding numerical method is of
order 푝. Larger values of 푝 correspond to faster convergence to the root. The
order of convergence of bisection is one: the error is reduced by approximately
a factor of 2 with each iteration so that
|휖푛+1| =
1
2
|휖푛|.
We now find the order of convergence for Newton’s Method and for the Secant
Method.
2.4.1 Newton’s Method
We start with Newton’s Method
푥푛+1 = 푥푛 −
푓(푥푛)
푓′(푥푛)
.
Subtracting both sides from 푟, we have
푟 − 푥푛+1 = 푟 − 푥푛 +
푓(푥푛)
푓′(푥푛)
,
or
휖푛+1 = 휖푛 +
푓(푥푛)
푓′(푥푛)
. (2.1)

We use Taylor series to expand the functions 푓(푥푛) and 푓′(푥푛) about the root
푟, using 푓(푟) = 0. We have
푓(푥푛) = 푓(푟) + (푥푛 − 푟)푓′(푟) +
1
2
(푥푛 − 푟)2푓′′(푟) + . . . ,
= −휖푛푓′(푟) +
1
2
휖2
푛푓′′(푟) + . . . ;
푓′(푥푛) = 푓′(푟) + (푥푛 − 푟)푓′′(푟) +
1
2
(푥푛 − 푟)2푓′′′(푟) + . . . ,
= 푓′(푟) − 휖푛푓′′(푟) +
1
2
휖2
푛푓′′′(푟) + . . . .
(2.2)
To make further progress, we will make use of the following standard Taylor
series:
1
1 − 휖
= 1 + 휖 + 휖2 + . . . , (2.3)
which converges for |휖| 1. Substituting (2.2) into (2.1), and using (2.3) yields
휖푛+1 = 휖푛 +
푓(푥푛)
푓′(푥푛)
= 휖푛 +
−휖푛푓′(푟) + 1
2 휖2
푛푓′′(푟) + . . .
푓′(푟) − 휖푛푓′′(푟) + 1
2 휖2
푛푓′′′(푟) + . . .
= 휖푛 +
−휖푛 + 1
2 휖2
푛
푓′′(푟)
푓′(푟) + . . .
1 − 휖푛
푓′′(푟)
푓′(푟) + . . .
= 휖푛 +
(︂
−휖푛 +
1
2
휖2
푛
푓′′(푟)
푓′(푟)
+ . . .
)︂(︂
1 + 휖푛
푓′′(푟)
푓′(푟)
+ . . .
)︂
= 휖푛 +
(︂
−휖푛 + 휖2
푛
(︂
1
2
푓′′(푟)
푓′(푟)
−
푓′′(푟)
푓′(푟)
)︂
+ . . .
)︂
= −
1
2
푓′′(푟)
푓′(푟)
휖2
푛 + . . .
Therefore, we have shown that
|휖푛+1| = 푘|휖푛|2
as 푛 → ∞, with
푘 =
1
2
⃒⃒⃒⃒
푓′′(푟)
푓′(푟)
⃒⃒⃒⃒
,
provided 푓′(푟)̸= 0. Newton’s method is thus of order 2 at simple roots.
2.4.2 Secant Method
Determining the order of the Secant Method proceeds in a similar fashion. We
start with
푥푛+1 = 푥푛 −
(푥푛 − 푥푛−1)푓(푥푛)
푓(푥푛) − 푓(푥푛−1)
.
We subtract both sides from 푟 and make use of
푥푛 − 푥푛−1 = (푟 − 푥푛−1) − (푟 − 푥푛)
= 휖푛−1 − 휖푛,

2.4. ORDER OF CONVERGENCE 11
and the Taylor series
푓(푥푛) = −휖푛푓′(푟) +
1
2
휖2
푛푓′′(푟) + . . . ,
푓(푥푛−1) = −휖푛−1푓′(푟) +
1
2
휖2
푛−1푓′′(푟) + . . . ,
so that
푓(푥푛) − 푓(푥푛−1) = (휖푛−1 − 휖푛)푓′(푟) +
1
2
(휖2
푛 − 휖2
푛−1)푓′′(푟) + . . .
= (휖푛−1 − 휖푛)
(︂
푓′(푟) −
1
2
(휖푛−1 + 휖푛)푓′′(푟) + . . .
)︂
.
We therefore have
휖푛+1 = 휖푛 +
−휖푛푓′(푟) + 1
2 휖2
푛푓′′(푟) + . . .
푓′(푟) − 1
2 (휖푛−1 + 휖푛)푓′′(푟) + . . .
= 휖푛 − 휖푛
1 − 1
2 휖푛
푓′′(푟)
푓′(푟) + . . .
2 (휖푛−1 + 휖푛) 푓′′(푟)
푓′(푟) + . . .
1 − 1
= 휖푛 − 휖푛
(︂
1 −
1
2
휖푛
푓′′(푟)
푓′(푟)
+ . . .
)︂(︂
1 +
1
2
(휖푛−1 + 휖푛)
푓′′(푟)
푓′(푟)
+ . . .
)︂
= −
1
2
푓′′(푟)
푓′(푟)
휖푛−1휖푛 + . . . ,
or to leading order
|휖푛+1| =
1
2
⃒⃒⃒⃒
푓′′(푟)
푓′(푟)
⃒⃒⃒⃒
|휖푛−1||휖푛|. (2.4)
The order of convergence is not yet obvious from this equation, and to determine
the scaling law we look for a solution of the form
|휖푛+1| = 푘|휖푛|푝.
From this ansatz, we also have
|휖푛| = 푘|휖푛−1|푝,
and therefore
|휖푛+1| = 푘푝+1|휖푛−1|푝2
.
Substitution into (2.4) results in
푘푝+1|휖푛−1|푝2
=
푘
2
⃒⃒⃒⃒
푓′′(푟)
푓′(푟)
⃒⃒⃒⃒
|휖푛−1|푝+1.
Equating the coefficient and the power of 휖푛−1 results in
푘푝 =
1
2
⃒⃒⃒⃒
푓′′(푟)
푓′(푟)
⃒⃒⃒⃒
,
and
푝2 = 푝 + 1.

The order of convergence of the Secant Method, given by 푝, therefore is deter-mined
to be the positive root of the quadratic equation 푝2 − 푝 − 1 = 0, or
푝 =
√
5
2
1 +
≈ 1.618,
which coincidentally is a famous irrational number that is called The Golden
Ratio, and goes by the symbol Φ. We see that the Secant Method has an order
of convergence lying between the Bisection Method and Newton’s Method.

Chapter 3
Systems of equations
Consider the system of 푛 linear equations and 푛 unknowns, given by
푎11푥1 + 푎12푥2 + · · · + 푎1푛푥푛 = 푏1,
푎21푥1 + 푎22푥2 + · · · + 푎2푛푥푛 = 푏2,
...
...
푎푛1푥1 + 푎푛2푥2 + · · · + 푎푛푛푥푛 = 푏푛.
We can write this system as the matrix equation
Ax = b,
with
A =
⎛
⎜⎜⎜⎝
푎11 푎12 · · · 푎1푛
푎21 푎22 · · · 푎2푛
...
...
. . .
...
푎푛1 푎푛2 · · · 푎푛푛
⎞
⎟⎟⎟⎠
, x =
⎛
⎜⎜⎜⎝
푥1
푥2
...
푥푛
⎞
⎟⎟⎟⎠
, b =
⎛
⎜⎜⎜⎝
푏1
푏2
...
푏푛
⎞
⎟⎟⎟⎠
.
3.1 Gaussian Elimination
The standard numerical algorithm to solve a system of linear equations is called
Gaussian Elimination. We can illustrate this algorithm by example.
Consider the system of equations
−3푥1 + 2푥2 − 푥3 = −1,
6푥1 − 6푥2 + 7푥3 = −7,
3푥1 − 4푥2 + 4푥3 = −6.
To perform Gaussian elimination, we form an Augmented Matrix by combining
the matrix A with the column vector b:
⎛
⎝
⎞
⎠.
−3 2 −1 −1
6 −6 7 −7
3 −4 4 −6
Row reduction is then performed on this matrix. Allowed operations are (1)
multiply any row by a constant, (2) add multiple of one row to another row, (3)
13

14 CHAPTER 3. SYSTEMS OF EQUATIONS
interchange the order of any rows. The goal is to convert the original matrix
into an upper-triangular matrix.
We start with the first row of the matrix and work our way down as follows.
First we multiply the first row by 2 and add it to the second row, and add the
first row to the third row:
⎛
⎝
−3 2 −1 −1
0 −2 5 −9
0 −2 3 −7
⎞
⎠.
We then go to the second row. We multiply this row by −1 and add it to the
third row: ⎛
−3 2 −1 −1
0 −2 5 −9
0 0 −2 2
⎝
⎞
⎠.
The resulting equations can be determined from the matrix and are given by
−3푥1 + 2푥2 − 푥3 = −1
−2푥2 + 5푥3 = −9
−2푥3 = 2.
These equations can be solved by backward substitution, starting from the last
equation and working backwards. We have
−2푥3 = 2 → 푥3 = −1
−2푥2 = −9 − 5푥3 = −4 → 푥2 = 2,
−3푥1 = −1 − 2푥2 + 푥3 = −6 → 푥1 = 2.
Therefore, ⎛
푥1
푥2
푥3
⎝
⎞
⎠ =
⎛
⎝
⎞
⎠.
2
2
−1
3.2 퐿푈 decomposition
The process of Gaussian Elimination also results in the factoring of the matrix
A to
A = LU,
where L is a lower triangular matrix and U is an upper triangular matrix.
Using the same matrix A as in the last section, we show how this factorization
is realized. We have
⎛
⎝
⎞
⎠ →
−3 2 −1
6 −6 7
3 −4 4
⎛
⎝
−3 2 −1
0 −2 5
0 −2 3
⎞
⎠ = M1A,
where
M1A =
⎛
⎝
⎞
⎠
1 0 0
2 1 0
1 0 1
⎛
⎝
⎞
⎠ =
−3 2 −1
6 −6 7
3 −4 4
⎛
⎝
⎞
⎠.
−3 2 −1
0 −2 5
0 −2 3

3.2. 퐿푈 DECOMPOSITION 15
Note that the matrix M1 performs row elimination on the first column. Two
times the first row is added to the second row and one times the first row is
added to the third row. The entries of the column of M1 come from 2 = −(6/−3)
and 1 = −(3/−3) as required for row elimination. The number −3 is called the
pivot.
The next step is
⎛
⎝
⎞
⎠ →
−3 2 −1
0 −2 5
0 −2 3
⎛
⎝
⎞
⎠ = M2(M1A),
−3 2 −1
0 −2 5
0 0 −2
where
M2(M1A) =
⎛
⎝
⎞
⎠
1 0 0
0 1 0
0 −1 1
⎛
⎝
⎞
⎠ =
−3 2 −1
0 −2 5
0 −2 3
⎛
⎝
⎞
⎠.
−3 2 −1
0 −2 5
0 0 −2
Here, M2 multiplies the second row by −1 = −(−2/ − 2) and adds it to the
third row. The pivot is −2.
We now have
M2M1A = U
or
A = M−1
1 M−1
2 U.
The inverse matrices are easy to find. The matrix M1 multiples the first row by
2 and adds it to the second row, and multiplies the first row by 1 and adds it
to the third row. To invert these operations, we need to multiply the first row
by −2 and add it to the second row, and multiply the first row by −1 and add
it to the third row. To check, with
M1M−1
1 = I,
we have ⎛
⎝
⎞
⎠
1 0 0
2 1 0
1 0 1
⎛
⎝
⎞
⎠ =
1 0 0
−2 1 0
−1 0 1
⎛
⎝
1 0 0
0 1 0
0 0 1
⎞
⎠.
Similarly,
M−1
2 =
⎛
⎝
1 0 0
0 1 0
0 1 1
⎞
⎠
Therefore,
L = M−1
1 M−1
2
is given by
L =
⎛
⎝
⎞
⎠
1 0 0
−2 1 0
−1 0 1
⎛
⎝
⎞
⎠ =
1 0 0
0 1 0
0 1 1
⎛
⎝
⎞
⎠,
1 0 0
−2 1 0
−1 1 1
which is lower triangular. The off-diagonal elements of M−1
1 and M−1
2 are simply
combined to form L. Our LU decomposition is therefore
⎛
⎝
−3 2 −1
6 −6 7
3 −4 4
⎞
⎠ =
⎛
⎝
⎞
⎠
1 0 0
−2 1 0
−1 1 1
⎛
⎝
⎞
⎠.
−3 2 −1
0 −2 5
0 0 −2

Another nice feature of the LU decomposition is that it can be done by over-writing
A, therefore saving memory if the matrix A is very large.
The LU decomposition is useful when one needs to solve Ax = b for x when
A is fixed and there are many different b’s. First one determines L and U using
Gaussian elimination. Then one writes
(LU)x = L(Ux) = b.
We let
y = Ux,
and first solve
Ly = b
for y by forward substitution. We then solve
Ux = y
for x by backward substitution. When we count operations, we will see that
solving (LU)x = b is significantly faster once L and U are in hand than solving
Ax = b directly by Gaussian elimination.
We now illustrate the solution of LUx = b using our previous example,
where
L =
⎛
⎝
⎞
⎠, U =
1 0 0
−2 1 0
−1 1 1
⎛
⎝
⎞
⎠, b =
−3 2 −1
0 −2 5
0 0 −2
⎛
⎝
−1
−7
−6
⎞
⎠.
With y = Ux, we first solve Ly = b, that is
⎛
⎝
⎞
⎠
1 0 0
−2 1 0
−1 1 1
⎛
⎝
푦1
푦2
푦3
⎞
⎠ =
⎛
⎝
⎞
⎠.
−1
−7
−6
Using forward substitution
푦1 = −1,
푦2 = −7 + 2푦1 = −9,
푦3 = −6 + 푦1 − 푦2 = 2.
We now solve Ux = y, that is
⎛
⎝
⎞
⎠
−3 2 −1
0 −2 5
0 0 −2
⎛
⎝
푥1
푥2
푥3
⎞
⎠ =
⎛
⎝
−1
−9
2
⎞
⎠.
Using backward substitution,
−2푥3 = 2 → 푥3 = −1,
−2푥2 = −9 − 5푥3 = −4 → 푥2 = 2,
−3푥1 = −1 − 2푥2 + 푥3 = −6 → 푥1 = 2,
and we have once again determined
⎛
⎝
푥1
푥2
푥3
⎞
⎠ =
⎛
⎝
⎞
⎠.
2
2
−1

3.3. PARTIAL PIVOTING 17
3.3 Partial pivoting
When performing Gaussian elimination, the diagonal element that one uses
during the elimination procedure is called the pivot. To obtain the correct
multiple, one uses the pivot as the divisor to the elements below the pivot.
Gaussian elimination in this form will fail if the pivot is zero. In this situation,
a row interchange must be performed.
Even if the pivot is not identically zero, a small value can result in big round-off
errors. For very large matrices, one can easily lose all accuracy in the solution.
To avoid these round-off errors arising from small pivots, row interchanges are
made, and this technique is called partial pivoting (partial pivoting is in contrast
to complete pivoting, where both rows and columns are interchanged). We will
illustrate by example the LU decomposition using partial pivoting.
Consider
A =
⎛
⎝
⎞
⎠.
−2 2 −1
6 −6 7
3 −8 4
We interchange rows to place the largest element (in absolute value) in the pivot,
or 푎11, position. That is,
A →
⎛
⎝
⎞
⎠ = P12A,
6 −6 7
−2 2 −1
3 −8 4
where
P12 =
⎛
⎝
0 1 0
1 0 0
0 0 1
⎞
⎠
is a permutation matrix that when multiplied on the left interchanges the first
and second rows of a matrix. Note that P−1
12 = P12. The elimination step is
then
P12A →
⎛
⎝
6 −6 7
0 0 4/3
0 −5 1/2
⎞
⎠ = M1P12A,
where
M1 =
⎛
⎝
⎞
⎠.
1 0 0
1/3 1 0
−1/2 0 1
The final step requires one more row interchange:
M1P12A →
⎛
⎝
⎞
⎠ = P23M1P12A = U.
6 −6 7
0 −5 1/2
0 0 4/3
Since the permutation matrices given by P are their own inverses, we can write
our result as
(P23M1P23)P23P12A = U.

Multiplication of M on the left by P interchanges rows while multiplication on
the right by P interchanges columns. That is,
P23
⎛
⎝
⎞
⎠P23 =
1 0 0
1/3 1 0
−1/2 0 1
⎛
⎝
1 0 0
−1/2 0 1
1/3 1 0
⎞
⎠P23 =
⎛
⎝
⎞
⎠.
1 0 0
−1/2 1 0
1/3 0 1
The net result on M1 is an interchange of the nondiagonal elements 1/3 and
−1/2.
We can then multiply by the inverse of (P23M1P23) to obtain
P23P12A = (P23M1P23)−1U,
which we write as
PA = LU.
Instead of L, MATLAB will write this as
A = (P−1L)U.
For convenience, we will just denote (P−1L) by L, but by L here we mean a
permutated lower triangular matrix.
For example, in MATLAB, to solve Ax = b for x using Gaussian elimination,
one types
x = A ∖ b;
where ∖ solves for x using the most efficient algorithm available, depending on
the form of A. If A is a general 푛 × 푛 matrix, then first the LU decomposition
of A is found using partial pivoting, and then 푥 is determined from permuted
forward and backward substitution. If A is upper or lower triangular, then
forward or backward substitution (or their permuted version) is used directly.
If there are many different right-hand-sides, one would first directly find the
LU decomposition of A using a function call, and then solve using ∖. That is,
one would iterate for different b’s the following expressions:
[LU] = lu(A);
y = L ∖ b;
x = U ∖ 푦;
where the second and third lines can be shortened to
x = U ∖ (L ∖ b);
where the parenthesis are required. In lecture, I will demonstrate these solutions
in MATLAB using the matrix A = [−2, 2,−1; 6,−6, 7; 3,−8, 4]; which is the
example in the notes.
3.4 Operation counts
To estimate how much computational time is required for an algorithm, one can
count the number of operations required (multiplications, divisions, additions
and subtractions). Usually, what is of interest is how the algorithm scales with

3.4. OPERATION COUNTS 19
the size of the problem. For example, suppose one wants to multiply two full
푛 × 푛 matrices. The calculation of each element requires 푛 multiplications and
푛 − 1 additions, or say 2푛 − 1 operations. There are 푛2 elements to compute
so that the total operation count is 푛2(2푛 − 1). If 푛 is large, we might want to
know what will happen to the computational time if 푛 is doubled. What matters
most is the fastest-growing, leading-order term in the operation count. In this
matrix multiplication example, the operation count is 푛2(2푛 − 1) = 2푛3 − 푛2,
and the leading-order term is 2푛3. The factor of 2 is unimportant for the scaling,
and we say that the algorithm scales like O(푛3), which is read as “big Oh of n
cubed.” When using the big-Oh notation, we will drop both lower-order terms
and constant multipliers.
The big-Oh notation tells us how the computational time of an algorithm
scales. For example, suppose that the multiplication of two large 푛×푛 matrices
took a computational time of 푇푛 seconds. With the known operation count
going like O(푛3), we can write
푇푛 = 푘푛3
for some unknown constant 푘. To determine how long multiplication of two
2푛 × 2푛 matrices will take, we write
푇2푛 = 푘(2푛)3
= 8푘푛3
= 8푇푛,
so that doubling the size of the matrix is expected to increase the computational
time by a factor of 23 = 8.
Running MATLAB on my computer, the multiplication of two 2048 × 2048
matrices took about 0.75 sec. The multiplication of two 4096 × 4096 matrices
took about 6 sec, which is 8 times longer. Timing of code in MATLAB can be
found using the built-in stopwatch functions tic and toc.
What is the operation count and therefore the scaling of Gaussian elimina-tion?
Consider an elimination step with the pivot in the 푖th row and 푖th column.
There are both 푛−푖 rows below the pivot and 푛−푖 columns to the right of the
pivot. To perform elimination of one row, each matrix element to the right of
the pivot must be multiplied by a factor and added to the row underneath. This
must be done for all the rows. There are therefore (푛−푖)(푛−푖) multiplication-additions
to be done for this pivot. Since we are interested in only the scaling
of the algorithm, I will just count a multiplication-addition as one operation.
To find the total operation count, we need to perform elimination using 푛−1
pivots, so that
op. counts =
푛Σ︁−1
푖=1
(푛 − 푖)2
= (푛 − 1)2 + (푛 − 2)2 + . . . (1)2
=
푛Σ︁−1
푖=1
푖2.

Three summation formulas will come in handy. They are
Σ︁푛
푖=1
1 = 푛,
Σ︁푛
푖=1
푖 =
1
2
푛(푛 + 1),
Σ︁푛
푖=1
푖2 =
1
6
푛(2푛 + 1)(푛 + 1),
which can be proved by mathematical induction, or derived by some tricks.
The operation count for Gaussian elimination is therefore
op. counts =
푛Σ︁−1
푖=1
푖2
=
1
6
(푛 − 1)(2푛 − 1)(푛).
The leading-order term is therefore 푛3/3, and we say that Gaussian elimination
scales like O(푛3). Since LU decomposition with partial pivoting is essentially
Gaussian elimination, that algorithm also scales like O(푛3).
However, once the LU decomposition of a matrix A is known, the solution
of Ax = b can proceed by a forward and backward substitution. How does
a backward substitution, say, scale? For backward substitution, the matrix
equation to be solved is of the form
⎛
푎1,1 푎1,2 · · · 푎1,푛−1 푎1,푛
0 푎2,2 · · · 푎2,푛−1 푎2,푛
⎜⎜⎜⎜⎜⎝
...
...
. . .
...
...
0 0 · · · 푎푛−1,푛−1 푎푛−1,푛
0 0 · · · 0 푎푛,푛
⎞
⎟⎟⎟⎟⎟⎠
⎛
⎜⎜⎜⎜⎜⎝
푥1
푥2
...
푥푛−1
푥푛
⎞
⎟⎟⎟⎟⎟⎠
=
⎛
⎜⎜⎜⎜⎜⎝
푏1
푏2
...
푏푛−1
푏푛
⎞
⎟⎟⎟⎟⎟⎠
.
The solution for 푥푖 is found after solving for 푥푗 with 푗 푖. The explicit solution
for 푥푖 is given by
푥푖 =
1
푎푖,푖
⎛
⎝푏푖 −
Σ︁푛
푗=푖+1
푎푖,푗푥푗
⎞
⎠.
The solution for 푥푖 requires 푛 − 푖 + 1 multiplication-additions, and since this
must be done for 푛 such 푖′푠, we have
op. counts =
Σ︁푛
푖=1
푛 − 푖 + 1
= 푛 + (푛 − 1) + · · · + 1
=
Σ︁푛
푖=1
푖
=
1
2
푛(푛 + 1).

3.5. SYSTEM OF NONLINEAR EQUATIONS 21
The leading-order term is 푛2/2 and the scaling of backward substitution is
O(푛2). After the LU decomposition of a matrix A is found, only a single forward
and backward substitution is required to solve Ax = b, and the scaling of the
algorithm to solve this matrix equation is therefore still O(푛2). For large 푛,
one should expect that solving Ax = b by a forward and backward substitution
should be substantially faster than a direct solution using Gaussian elimination.
3.5 System of nonlinear equations
A system of nonlinear equations can be solved using a version of Newton’s
Method. We illustrate this method for a system of two equations and two
unknowns. Suppose that we want to solve
푓(푥, 푦) = 0, 푔(푥, 푦) = 0,
for the unknowns 푥 and 푦. We want to construct two simultaneous sequences
푥0, 푥1, 푥2, . . . and 푦0, 푦1, 푦2, . . . that converge to the roots. To construct these
sequences, we Taylor series expand 푓(푥푛+1, 푦푛+1) and 푔(푥푛+1, 푦푛+1) about the
point (푥푛, 푦푛). Using the partial derivatives 푓푥 = 휕푓/휕푥, 푓푦 = 휕푓/휕푦, etc., the
two-dimensional Taylor series expansions, displaying only the linear terms, are
given by
푓(푥푛+1, 푦푛+1) = 푓(푥푛, 푦푛) + (푥푛+1 − 푥푛)푓푥(푥푛, 푦푛)
+ (푦푛+1 − 푦푛)푓푦(푥푛, 푦푛) + . . .
푔(푥푛+1, 푦푛+1) = 푔(푥푛, 푦푛) + (푥푛+1 − 푥푛)푔푥(푥푛, 푦푛)
+ (푦푛+1 − 푦푛)푔푦(푥푛, 푦푛) + . . . .
To obtain Newton’s method, we take 푓(푥푛+1, 푦푛+1) = 0, 푔(푥푛+1, 푦푛+1) = 0 and
drop higher-order terms above linear. Although one can then find a system of
linear equations for 푥푛+1 and 푦푛+1, it is more convenient to define the variables
Δ푥푛 = 푥푛+1 − 푥푛, Δ푦푛 = 푦푛+1 − 푦푛.
The iteration equations will then be given by
푥푛+1 = 푥푛 + Δ푥푛, 푦푛+1 = 푦푛 + Δ푦푛;
and the linear equations to be solved for Δ푥푛 and Δ푦푛 are given by
(︂
푓푥 푓푦
푔푥 푔푦
)︂(︂
Δ푥푛
Δ푦푛
)︂
=
(︂
−푓
−푔
)︂
,
where 푓, 푔, 푓푥, 푓푦, 푔푥, and 푔푦 are all evaluated at the point (푥푛, 푦푛). The two-dimensional
case is easily generalized to 푛 dimensions. The matrix of partial
derivatives is called the Jacobian Matrix.
We illustrate Newton’s Method by finding the steady state solution of the
Lorenz equations, given by
휎(푦 − 푥) = 0,
푟푥 − 푦 − 푥푧 = 0,
푥푦 − 푏푧 = 0,

where 푥, 푦, and 푧 are the unknown variables and 휎, 푟, and 푏 are the known
parameters. Here, we have a three-dimensional homogeneous system 푓 = 0,
푔 = 0, and ℎ = 0, with
푓(푥, 푦, 푧) = 휎(푦 − 푥),
푔(푥, 푦, 푧) = 푟푥 − 푦 − 푥푧,
ℎ(푥, 푦, 푧) = 푥푦 − 푏푧.
The partial derivatives can be computed to be
푓푥 = −휎, 푓푦 = 휎, 푓푧 = 0,
푔푥 = 푟 − 푧, 푔푦 = −1, 푔푧 = −푥,
ℎ푥 = 푦, ℎ푦 = 푥, ℎ푧 = −푏.
The iteration equation is therefore
⎛
⎝
−휎 휎 0
푟 − 푧푛 −1 −푥푛
푦푛 푥푛 −푏
⎞
⎠
⎛
⎝
Δ푥푛
Δ푦푛
Δ푧푛
⎞
⎠ = −
⎛
⎝
휎(푦푛 − 푥푛)
푟푥푛 − 푦푛 − 푥푛푧푛
푥푛푦푛 − 푏푧푛
⎞
⎠,
with
푥푛+1 = 푥푛 + Δ푥푛,
푦푛+1 = 푦푛 + Δ푦푛,
푧푛+1 = 푧푛 + Δ푧푛.
The MATLAB program that solves this system is contained in newton system.m.

Chapter 4
Least-squares
approximation
The method of least-squares is commonly used to fit a parameterized curve to
experimental data. In general, the fitting curve is not expected to pass through
the data points, making this problem substantially different from the one of
interpolation.
We consider here only the simplest case of the same experimental error for
all the data points. Let the data to be fitted be given by (푥푖, 푦푖), with 푖 = 1 to
푛.
4.1 Fitting a straight line
Suppose the fitting curve is a line. We write for the fitting curve
푦(푥) = 훼푥 + 훽.
The distance 푟푖 from the data point (푥푖, 푦푖) and the fitting curve is given by
푟푖 = 푦푖 − 푦(푥푖)
= 푦푖 − (훼푥푖 + 훽).
A least-squares fit minimizes the sum of the squares of the 푟푖’s. This minimum
can be shown to result in the most probable values of 훼 and 훽.
We define
휌 =
Σ︁푛
푖=1
푟2
푖
=
Σ︁푛
푖=1
(︀
푦푖 − (훼푥푖 + 훽)
)︀2
.
To minimize 휌 with respect to 훼 and 훽, we solve
휕휌
휕훼
= 0,
휕휌
휕훽
= 0.
23

24 CHAPTER 4. LEAST-SQUARES APPROXIMATION
Taking the partial derivatives, we have
휕휌
휕훼
=
Σ︁푛
푖=1
(︀
2(−푥푖)
)︀
= 0,
휕휌
휕훽
=
Σ︁푛
푖=1
2(−1)
(︀
)︀
= 0.
These equations form a system of two linear equations in the two unknowns 훼
and 훽, which is evident when rewritten in the form
훼
Σ︁푛
푖=1
푥2푖
+ 훽
Σ︁푛
푖=1
푥푖 =
Σ︁푛
푖=1
푥푖푦푖,
훼
Σ︁푛
푖=1
푥푖 + 훽푛 =
Σ︁푛
푖=1
푦푖.
These equations can be solved either analytically, or numerically in MATLAB,
where the matrix form is (︂Σ︀푛
푖=1 푥2푖
Σ︀푛
푖=1 Σ︀ 푥푖 푛
푖=1 푥푖 푛
)︂(︂
훼
훽
)︂
=
(︂Σ︀푛
푖=1 Σ︀ 푥푖푦푖 푛
푖=1 푦푖
)︂
.
A proper statistical treatment of this problem should also consider an estimate
of the errors in 훼 and 훽 as well as an estimate of the goodness-of-fit of the data
to the model. We leave these further considerations to a statistics class.
4.2 Fitting to a linear combination of functions
Consider the general fitting function
푦(푥) =
Σ︁푚
푗=1
푐푗푓푗(푥),
where we assume 푚 functions 푓푗(푥). For example, if we want to fit a cubic
polynomial to the data, then we would have 푚 = 4 and take 푓1 = 1, 푓2 = 푥,
푓3 = 푥2 and 푓4 = 푥3. Typically, the number of functions 푓푗 is less than the
number of data points; that is, 푚 푛, so that a direct attempt to solve for
the 푐푗 ’s such that the fitting function exactly passes through the 푛 data points
would result in 푛 equations and 푚 unknowns. This would be an over-determined
linear system that in general has no solution.
We now define the vectors
y =
⎛
⎜⎜⎜⎝
푦1
푦2
...
푦푛
⎞
⎟⎟⎟⎠
, c =
⎛
⎜⎜⎜⎝
푐1
푐2
...
푐푚
⎞
⎟⎟⎟⎠
,
and the 푛-by-푚 matrix
A =
⎛
⎜⎜⎜⎝
푓1(푥1) 푓2(푥1) · · · 푓푚(푥1)
푓1(푥2) 푓2(푥2) · · · 푓푚(푥2)
...
...
. . .
...
⎞
⎟⎟⎟⎠
푓1(푥푛) 푓2(푥푛) · · · 푓푚(푥푛)
. (4.1)

4.2. FITTING TO A LINEAR COMBINATION OF FUNCTIONS 25
With 푚 푛, the equation Ac = y is over-determined. We let
r = y − Ac
be the residual vector, and let
휌 =
Σ︁푛
푖=1
푟2
푖 .
The method of least squares minimizes 휌 with respect to the components of c.
Now, using 푇 to signify the transpose of a matrix, we have
휌 = r푇 r
= (y − Ac)푇 (y − Ac)
= y푇 y − c푇A푇 y − y푇Ac + c푇A푇Ac.
Since 휌 is a scalar, each term in the above expression must be a scalar, and since
the transpose of a scalar is equal to the scalar, we have
c푇A푇 y =
(︀
c푇A푇 y
)︀푇
= y푇Ac.
Therefore,
휌 = y푇 y − 2y푇Ac + c푇A푇Ac.
To find the minimum of 휌, we will need to solve 휕휌/휕푐푗 = 0 for 푗 = 1, . . . ,푚.
To take the derivative of 휌, we switch to a tensor notation, using the Einstein
summation convention, where repeated indices are summed over their allowable
range. We can write
휌 = 푦푖푦푖 − 2푦푖A푖푘푐푘 + 푐푖A푇
푖푘A푘푙푐푙.
Taking the partial derivative, we have
휕휌
휕푐푗
= −2푦푖A푖푘
휕푐푘
휕푐푗
+
휕푐푖
휕푐푗
푖푘A푘푙푐푙 + 푐푖A푇
A푇
푖푘A푘푙
휕푐푙
휕푐푗
.
Now,
휕푐푖
휕푐푗
=
{︃
1, if 푖 = 푗;
0, otherwise.
Therefore,
휕휌
휕푐푗
= −2푦푖A푖푗 + A푇푗
푘A푘푙푐푙 + 푐푖A푇
푖푘A푘푗 .
Now,
푐푖A푇
푖푘A푘푗 = 푐푖A푘푖A푘푗
= A푘푗A푘푖푐푖
= A푇푗
푘A푘푖푐푖
= A푇푗
푘A푘푙푐푙.

26 CHAPTER 4. LEAST-SQUARES APPROXIMATION
Therefore,
휕휌
휕푐푗
= −2푦푖A푖푗 + 2A푇푗
푘A푘푙푐푙.
With the partials set equal to zero, we have
A푇푗
푘A푘푙푐푙 = 푦푖A푖푗 ,
or
A푇푗
푘A푘푙푐푙 = A푇푗
푖푦푖,
In vector notation, we have
A푇Ac = A푇 y. (4.2)
Equation (4.2) is the so-called normal equation, and can be solved for c by
Gaussian elimination using the MATLAB backslash operator. After construct-ing
the matrix A given by (4.1), and the vector y from the data, one can code
in MATLAB
푐 = (퐴′퐴)∖(퐴′푦);
But in fact the MATLAB back slash operator will automatically solve the normal
equations when the matrix A is not square, so that the MATLAB code
푐 = 퐴∖푦;
yields the same result.

Chapter 5
Interpolation
Consider the following problem: Given the values of a known function 푦 = 푓(푥)
at a sequence of ordered points 푥0, 푥1, . . . , 푥푛, find 푓(푥) for arbitrary 푥. When
푥0 ≤ 푥 ≤ 푥푛, the problem is called interpolation. When 푥 푥0 or 푥 푥푛 the
problem is called extrapolation.
With 푦푖 = 푓(푥푖), the problem of interpolation is basically one of drawing a
smooth curve through the known points (푥0, 푦0), (푥1, 푦1), . . . , (푥푛, 푦푛). This is
not the same problem as drawing a smooth curve that approximates a set of data
points that have experimental error. This latter problem is called least-squares
approximation.
Here, we will consider three interpolation algorithms: (1) polynomial inter-polation;
(2) piecewise linear interpolation, and; (3) cubic spline interpolation.
5.1 Polynomial interpolation
The 푛 + 1 points (푥0, 푦0), (푥1, 푦1), . . . , (푥푛, 푦푛) can be interpolated by a unique
polynomial of degree 푛. When 푛 = 1, the polynomial is a linear function;
when 푛 = 2, the polynomial is a quadratic function. There are three standard
algorithms that can be used to construct this unique interpolating polynomial,
and we will present all three here, not so much because they are all useful, but
because it is interesting to learn how these three algorithms are constructed.
When discussing each algorithm, we define 푃푛(푥) to be the unique 푛th degree
polynomial that passes through the given 푛 + 1 data points.
5.1.1 Vandermonde polynomial
This Vandermonde polynomial is the most straightforward construction of the
interpolating polynomial 푃푛(푥). We simply write
푃푛(푥) = 푐0푥푛 + 푐1푥푛−1 + · · · + 푐푛.
27

28 CHAPTER 5. INTERPOLATION
Then we can immediately form 푛 + 1 linear equations for the 푛 + 1 unknown
coefficients 푐0, 푐1, . . . , 푐푛 using the 푛 + 1 known points:
푦0 = 푐0푥푛0
+ 푐1푥푛−1
0 + · · · + 푐푛−1푥0 + 푐푛
푦2 = 푐0푥푛1
+ 푐1푥푛−1
1 + · · · + 푐푛−1푥1 + 푐푛
...
...
...
푦푛 = 푐0푥푛푛
+ 푐1푥푛−1
푛 + · · · + 푐푛−1푥푛 + 푐푛.
The system of equations in matrix form is
⎛
푥푛0
⎜⎜⎜⎝
⎞
푥푛−1
0 · · · 푥0 1
푥푛1
⎟⎟⎟⎠
푥푛−1
1 · · · 푥1 1
...
...
. . .
...
푥푛푛
푥푛−1
푛 · · · 푥푛 1
⎛
⎜⎜⎜⎝
푐0
푐1
...
푐푛
⎞
⎟⎟⎟⎠
=
⎛
⎜⎜⎜⎝
푦0
푦1
...
푦푛
⎞
⎟⎟⎟⎠
.
The matrix is called the Vandermonde matrix, and can be constructed using
the MATLAB function vander.m. The system of linear equations can be solved
in MATLAB using the ∖ operator, and the MATLAB function polyval.m can
used to interpolate using the 푐 coefficients. I will illustrate this in class and
place the code on the website.
5.1.2 Lagrange polynomial
The Lagrange polynomial is the most clever construction of the interpolating
polynomial 푃푛(푥), and leads directly to an analytical formula. The Lagrange
polynomial is the sum of 푛 + 1 terms and each term is itself a polynomial of
degree 푛. The full polynomial is therefore of degree 푛. Counting from 0, the 푖th
term of the Lagrange polynomial is constructed by requiring it to be zero at 푥푗
with 푗̸= 푖, and equal to 푦푖 when 푗 = 푖. The polynomial can be written as
푃푛(푥) =
(푥 − 푥1)(푥 − 푥2) · · · (푥 − 푥푛)푦0
(푥0 − 푥1)(푥0 − 푥2) · · · (푥0 − 푥푛)
+
(푥 − 푥0)(푥 − 푥2) · · · (푥 − 푥푛)푦1
(푥1 − 푥0)(푥1 − 푥2) · · · (푥1 − 푥푛)
+ · · · +
(푥 − 푥0)(푥 − 푥1) · · · (푥 − 푥푛−1)푦푛
(푥푛 − 푥0)(푥푛 − 푥1) · · · (푥푛 − 푥푛−1)
.
It can be clearly seen that the first term is equal to zero when 푥 = 푥1, 푥2, . . . , 푥푛
and equal to 푦0 when 푥 = 푥0; the second term is equal to zero when 푥 =
푥0, 푥2, . . . 푥푛 and equal to 푦1 when 푥 = 푥1; and the last term is equal to zero
when 푥 = 푥0, 푥1, . . . 푥푛−1 and equal to 푦푛 when 푥 = 푥푛. The uniqueness of
the interpolating polynomial implies that the Lagrange polynomial must be the
interpolating polynomial.
5.1.3 Newton polynomial
The Newton polynomial is somewhat more clever than the Vandermonde poly-nomial
because it results in a system of linear equations that is lower triangular,
and therefore can be solved by forward substitution. The interpolating polyno-mial
is written in the form
푃푛(푥) = 푐0 + 푐1(푥 − 푥0) + 푐2(푥 − 푥0)(푥 − 푥1) + · · · + 푐푛(푥 − 푥0) · · · (푥 − 푥푛−1),

5.2. PIECEWISE LINEAR INTERPOLATION 29
which is clearly a polynomial of degree 푛. The 푛+1 unknown coefficients given
by the 푐’s can be found by substituting the points (푥푖, 푦푖) for 푖 = 0, . . . , 푛:
푦0 = 푐0,
푦1 = 푐0 + 푐1(푥1 − 푥0),
푦2 = 푐0 + 푐1(푥2 − 푥0) + 푐2(푥2 − 푥0)(푥2 − 푥1),
...
...
...
푦푛 = 푐0 + 푐1(푥푛 − 푥0) + 푐2(푥푛 − 푥0)(푥푛 − 푥1) + · · · + 푐푛(푥푛 − 푥0) · · · (푥푛 − 푥푛−1).
This system of linear equations is lower triangular as can be seen from the
matrix form
⎛
⎜⎜⎜⎝
1 0 0 · · · 0
1 (푥1 − 푥0) 0 · · · 0
...
...
...
. . .
...
1 (푥푛 − 푥0) (푥푛 − 푥0)(푥푛 − 푥1) · · · (푥푛 − 푥0) · · · (푥푛 − 푥푛−1)
⎞
⎟⎟⎟⎠
⎛
⎜⎜⎜⎝
푐0
푐1
...
푐푛
⎞
⎟⎟⎟⎠
=
⎛
⎜⎜⎜⎝
푦0
푦1
...
푦푛
⎞
⎟⎟⎟⎠
,
and so theoretically can be solved faster than the Vandermonde polynomial. In
practice, however, there is little difference because polynomial interpolation is
only useful when the number of points to be interpolated is small.
5.2 Piecewise linear interpolation
Instead of constructing a single global polynomial that goes through all the
points, one can construct local polynomials that are then connected together.
In the the section following this one, we will discuss how this may be done using
cubic polynomials. Here, we discuss the simpler case of linear polynomials. This
is the default interpolation typically used when plotting data.
Suppose the interpolating function is 푦 = 푔(푥), and as previously, there are
푛+1 points to interpolate. We construct the function 푔(푥) out of 푛 local linear
polynomials. We write
푔(푥) = 푔푖(푥), for 푥푖 ≤ 푥 ≤ 푥푖+1,
where
푔푖(푥) = 푎푖(푥 − 푥푖) + 푏푖,
and 푖 = 0, 1, . . . , 푛 − 1.
We now require 푦 = 푔푖(푥) to pass through the endpoints (푥푖, 푦푖) and (푥푖+1, 푦푖+1).
We have
푦푖 = 푏푖,
푦푖+1 = 푎푖(푥푖+1 − 푥푖) + 푏푖.

Therefore, the coefficients of 푔푖(푥) are determined to be
푎푖 =
푦푖+1 − 푦푖
푥푖+1 − 푥푖
, 푏푖 = 푦푖.
Although piecewise linear interpolation is widely used, particularly in plotting
routines, it suffers from a discontinuity in the derivative at each point. This
results in a function which may not look smooth if the points are too widely
spaced. We next consider a more challenging algorithm that uses cubic polyno-mials.
5.3 Cubic spline interpolation
The 푛 + 1 points to be interpolated are again
(푥0, 푦0), (푥1, 푦1), . . . (푥푛, 푦푛).
Here, we use 푛 piecewise cubic polynomials for interpolation,
푔푖(푥) = 푎푖(푥 − 푥푖)3 + 푏푖(푥 − 푥푖)2 + 푐푖(푥 − 푥푖) + 푑푖, 푖 = 0, 1, . . . , 푛 − 1,
with the global interpolation function written as
푔(푥) = 푔푖(푥), for 푥푖 ≤ 푥 ≤ 푥푖+1.
To achieve a smooth interpolation we impose that 푔(푥) and its first and
second derivatives are continuous. The requirement that 푔(푥) is continuous
(and goes through all 푛 + 1 points) results in the two constraints
푔푖(푥푖) = 푦푖, 푖 = 0 to 푛 − 1, (5.1)
푔푖(푥푖+1) = 푦푖+1, 푖 = 0 to 푛 − 1. (5.2)
The requirement that 푔′(푥) is continuous results in
푔′푖
(푥푖+1) = 푔′ 푖+1(푥푖+1), 푖 = 0 to 푛 − 2. (5.3)
And the requirement that 푔′′(푥) is continuous results in
푔′′
푖 (푥푖+1) = 푔′′
푖+1(푥푖+1), 푖 = 0 to 푛 − 2. (5.4)
There are 푛 cubic polynomials 푔푖(푥) and each cubic polynomial has four free
coefficients; there are therefore a total of 4푛 unknown coefficients. The number
of constraining equations from (5.1)-(5.4) is 2푛+2(푛−1) = 4푛−2. With 4푛−2
constraints and 4푛 unknowns, two more conditions are required for a unique
solution. These are usually chosen to be extra conditions on the first 푔0(푥) and
last 푔푛−1(푥) polynomials. We will discuss these extra conditions later.
We now proceed to determine equations for the unknown coefficients of the
cubic polynomials. The polynomials and their first two derivatives are given by
푖
′푔푖(푥) = 푎푖(푥 − 푥푖)3 + 푏푖(푥 − 푥2 푖)+ 푐푖(푥 − 푥푖) + 푑푖, (5.5)
푔(푥) = 3푎(푥 − 푥)2 푖푖+ 2푏푖(푥 − 푥푖) + 푐푖, (5.6)
푔′′
푖 (푥) = 6푎푖(푥 − 푥푖) + 2푏푖. (5.7)

5.3. CUBIC SPLINE INTERPOLATION 31
We will consider the four conditions (5.1)-(5.4) in turn. From (5.1) and (5.5),
we have
푑푖 = 푦푖, 푖 = 0 to 푛 − 1, (5.8)
which directly solves for all of the 푑-coefficients.
To satisfy (5.2), we first define
ℎ푖 = 푥푖+1 − 푥푖,
and
푓푖 = 푦푖+1 − 푦푖.
Now, from (5.2) and (5.5), using (5.8), we obtain the 푛 equations
푎푖ℎ3푖
+ 푏푖ℎ2푖
+ 푐푖ℎ푖 = 푓푖, 푖 = 0 to 푛 − 1. (5.9)
From (5.3) and (5.6) we obtain the 푛 − 1 equations
3푎푖ℎ2푖
+ 2푏푖ℎ푖 + 푐푖 = 푐푖+1, 푖 = 0 to 푛 − 2. (5.10)
From (5.4) and (5.7) we obtain the 푛 − 1 equations
3푎푖ℎ푖 + 푏푖 = 푏푖+1 푖 = 0 to 푛 − 2. (5.11)
It is will be useful to include a definition of the coefficient 푏푛, which is now
missing. (The index of the cubic polynomial coefficients only go up to 푛 − 1.)
We simply extend (5.11) up to 푖 = 푛 − 1 and so write
3푎푛−1ℎ푛−1 + 푏푛−1 = 푏푛, (5.12)
which can be viewed as a definition of 푏푛.
We now proceed to eliminate the sets of 푎- and 푐-coefficients (with the 푑-
coefficients already eliminated in (5.8)) to find a system of linear equations for
the 푏-coefficients. From (5.11) and (5.12), we can solve for the 푛 푎-coefficients
to find
푎푖 =
1
3ℎ푖
(푏푖+1 − 푏푖) , 푖 = 0 to 푛 − 1. (5.13)
From (5.9), we can solve for the 푛 푐-coefficients as follows:
푐푖 =
1
ℎ푖
(︀
푓푖 − 푎푖ℎ3푖
− 푏푖ℎ2푖
)︀
=
1
ℎ푖
(︂
푓푖 −
1
3ℎ푖
(푏푖+1 − 푏푖) ℎ3푖
− 푏푖ℎ2푖
)︂
=
푓푖
ℎ푖
−
1
3
ℎ푖 (푏푖+1 + 2푏푖) , 푖 = 0 to 푛 − 1. (5.14)
We can now find an equation for the 푏-coefficients by substituting (5.8),
(5.13) and (5.14) into (5.10):
3
(︂
1
3ℎ푖
)︂
ℎ2푖
(푏푖+1 − 푏푖)
+ 2푏푖ℎ푖 +
(︂
푓푖
ℎ푖
−
1
3
)︂
ℎ푖(푏푖+1 + 2푏푖)
=
(︂
푓푖+1
ℎ푖+1
−
1
3
)︂
,
ℎ푖+1(푏푖+2 + 2푏푖+1)

which simplifies to
1
3
ℎ푖푏푖 +
2
3
(ℎ푖 + ℎ푖+1)푏푖+1 +
1
3
ℎ푖+1푏푖+2 =
푓푖+1
ℎ푖+1
−
푓푖
ℎ푖
, (5.15)
an equation that is valid for 푖 = 0 to 푛 − 2. Therefore, (5.15) represent 푛 − 1
equations for the 푛+1 unknown 푏-coefficients. Accordingly, we write the matrix
equation for the 푏-coefficients, leaving the first and last row absent, as
⎛
⎜⎜⎜⎜⎜⎝
. . . . . . . . . . . . missing . . . . . .
1
ℎ2
0
(ℎ0 + ℎ1) 1
ℎ1 . . . 0 0 0
33 3...
...
...
. . .
...
...
...
0 0 0 . . . 1
3ℎ푛−2
2
3 (ℎ푛−2 + ℎ푛−1) 1
3ℎ푛−1
. . . . . . . . . . . . missing . . . . . .
⎞
⎟⎟⎟⎟⎟⎠
⎛
⎜⎜⎜⎜⎜⎝
푏0
푏1
...
푏푛−1
푏푛
⎞
⎟⎟⎟⎟⎟⎠ =
⎛
⎜⎜⎜⎜⎜⎜⎝
missing
푓1
− 푓0
ℎ1
ℎ0
...
푓푛−1
ℎ푛−1
− 푓푛−2
ℎ푛−2
missing
⎞
⎟⎟⎟⎟⎟⎟⎠
.
Once the missing first and last equations are specified, the matrix equation
for the 푏-coefficients can be solved by Gaussian elimination. And once the 푏-
coefficients are determined, the 푎- and 푐-coefficients can also be determined from
(5.13) and (5.14), with the 푑-coefficients already known from (5.8). The piece-wise
cubic polynomials, then, are known and 푔(푥) can be used for interpolation
to any value 푥 satisfying 푥0 ≤ 푥 ≤ 푥푛.
The missing first and last equations can be specified in several ways, and
here we show the two ways that are allowed by the MATLAB function spline.m.
The first way should be used when the derivative 푔′(푥) is known at the endpoints
푥0 and 푥푛; that is, suppose we know the values of 훼 and 훽 such that
푔′0
(푥0) = 훼, 푔′푛
−1(푥푛) = 훽.
From the known value of 훼, and using (5.6) and (5.14), we have
훼 = 푐0
=
푓0
ℎ0
−
1
3
ℎ0(푏1 + 2푏0).
Therefore, the missing first equation is determined to be
2
3
ℎ0푏0 +
1
3
ℎ0푏1 =
푓0
ℎ0
− 훼. (5.16)
From the known value of 훽, and using (5.6), (5.13), and (5.14), we have
훽 = 3푎푛−1ℎ2
푛−1 + 2푏푛−1ℎ푛−1 + 푐푛−1
= 3
(︂
1
3ℎ푛−1
)︂
ℎ2
(푏푛 − 푏푛−1)
푛−1 + 2푏푛−1ℎ푛−1 +
(︂
푓푛−1
ℎ푛−1
−
1
3
)︂
,
ℎ푛−1(푏푛 + 2푏푛−1)

5.4. MULTIDIMENSIONAL INTERPOLATION 33
which simplifies to
1
3
ℎ푛−1푏푛−1 +
2
3
ℎ푛−1푏푛 = 훽 −
푓푛−1
ℎ푛−1
, (5.17)
to be used as the missing last equation.
The second way of specifying the missing first and last equations is called
the not-a-knot condition, which assumes that
푔0(푥) = 푔1(푥), 푔푛−2(푥) = 푔푛−1(푥).
Considering the first of these equations, from (5.5) we have
푎0(푥 − 푥0)3 + 푏0(푥 − 푥0)2 + 푐0(푥 − 푥0) + 푑0
= 푎1(푥 − 푥1)3 + 푏1(푥 − 푥1)2 + 푐1(푥 − 푥1) + 푑1.
Now two cubic polynomials can be proven to be identical if at some value of 푥,
the polynomials and their first three derivatives are identical. Our conditions of
continuity at 푥 = 푥1 already require that at this value of 푥 these two polynomials
and their first two derivatives are identical. The polynomials themselves will be
identical, then, if their third derivatives are also identical at 푥 = 푥1, or if
푎0 = 푎1.
From (5.13), we have
1
3ℎ0
(푏1 − 푏0) =
1
3ℎ1
(푏2 − 푏1),
or after simplification
ℎ1푏0 − (ℎ0 + ℎ1)푏1 + ℎ0푏2 = 0, (5.18)
which provides us our missing first equation. A similar argument at 푥 = 푥푛 −1
also provides us with our last equation,
ℎ푛−1푏푛−2 − (ℎ푛−2 + ℎ푛−1)푏푛−1 + ℎ푛−2푏푛 = 0. (5.19)
The MATLAB subroutines spline.m and ppval.m can be used for cubic spline
interpolation (see also interp1.m). I will illustrate these routines in class and
post sample code on the course web site.
5.4 Multidimensional interpolation
Suppose we are interpolating the value of a function of two variables,
푧 = 푓(푥, 푦).
The known values are given by
푧푖푗 = 푓(푥푖, 푦푗),
with 푖 = 0, 1, . . . , 푛 and 푗 = 0, 1, . . . , 푛. Note that the (푥, 푦) points at which
푓(푥, 푦) are known lie on a grid in the 푥 − 푦 plane.

Let 푧 = 푔(푥, 푦) be the interpolating function, satisfying 푧푖푗 = 푔(푥푖, 푦푗 ). A
two-dimensional interpolation to find the value of 푔 at the point (푥, 푦) may be
done by first performing 푛 + 1 one-dimensional interpolations in 푦 to find the
value of 푔 at the 푛 + 1 points (푥0, 푦), (푥1, 푦), . . . , (푥푛, 푦), followed by a single
one-dimensional interpolation in 푥 to find the value of 푔 at (푥, 푦).
In other words, two-dimensional interpolation on a grid of dimension (푛 +
1) × (푛 + 1) is done by first performing 푛 + 1 one-dimensional interpolations
to the value 푦 followed by a single one-dimensional interpolation to the value
푥. Two-dimensional interpolation can be generalized to higher dimensions. The
MATLAB functions to perform two- and three-dimensional interpolation are
interp2.m and interp3.m.

Chapter 6
Integration
We want to construct numerical algorithms that can perform definite integrals
of the form
퐼 =
∫︁ 푏
푎
푓(푥)푑푥. (6.1)
Calculating these definite integrals numerically is called numerical integration,
numerical quadrature, or more simply quadrature.
6.1 Elementary formulas
We first consider integration from 0 to ℎ, with ℎ small, to serve as the building
blocks for integration over larger domains. We here define 퐼ℎ as the following
integral:
퐼ℎ =
∫︁ ℎ
0
푓(푥)푑푥. (6.2)
To perform this integral, we consider a Taylor series expansion of 푓(푥) about
the value 푥 = ℎ/2:
푓(푥) = 푓(ℎ/2) + (푥 − ℎ/2)푓′(ℎ/2) +
(푥 − ℎ/2)2
2
푓′′(ℎ/2)
+
(푥 − ℎ/2)3
6
푓′′′(ℎ/2) +
(푥 − ℎ/2)4
24
푓′′′′(ℎ/2) + . . .
6.1.1 Midpoint rule
The midpoint rule makes use of only the first term in the Taylor series expansion.
Here, we will determine the error in this approximation. Integrating,
퐼ℎ = ℎ푓(ℎ/2) +
∫︁ ℎ
0
(︂
(푥 − ℎ/2)푓′(ℎ/2) +
(푥 − ℎ/2)2
2
푓′′(ℎ/2)
+
(푥 − ℎ/2)3
6
푓′′′(ℎ/2) +
(푥 − ℎ/2)4
24
푓′′′′(ℎ/2) + . . .
)︂
푑푥.
35

36 CHAPTER 6. INTEGRATION
Changing variables by letting 푦 = 푥 − ℎ/2 and 푑푦 = 푑푥, and simplifying the
integral depending on whether the integrand is even or odd, we have
퐼ℎ = ℎ푓(ℎ/2)
+
∫︁ ℎ/2
−ℎ/2
(︂
푦푓′(ℎ/2) +
푦2
2
푓′′(ℎ/2) +
푦3
6
푓′′′(ℎ/2) +
푦4
24
푓′′′′(ℎ/2) + . . .
)︂
푑푦
= ℎ푓(ℎ/2) +
∫︁ ℎ/2
0
(︂
푦2푓′′(ℎ/2) +
푦4
12
푓′′′′(ℎ/2) + . . .
)︂
푑푦.
The integrals that we need here are
∫︁ ℎ
2
0
푦2푑푦 =
ℎ3
24
,
∫︁ ℎ
2
0
푦4푑푦 =
ℎ5
160
.
Therefore,
퐼ℎ = ℎ푓(ℎ/2) +
ℎ3
24
푓′′(ℎ/2) +
ℎ5
1920
푓′′′′(ℎ/2) + . . . . (6.3)
6.1.2 Trapezoidal rule
From the Taylor series expansion of 푓(푥) about 푥 = ℎ/2, we have
푓(0) = 푓(ℎ/2) −
ℎ
2
푓′(ℎ/2) +
ℎ2
8
푓′′(ℎ/2) −
ℎ3
48
푓′′′(ℎ/2) +
ℎ4
384
푓′′′′(ℎ/2) + . . . ,
and
푓(ℎ) = 푓(ℎ/2) +
ℎ
2
푓′(ℎ/2) +
ℎ2
8
푓′′(ℎ/2) +
ℎ3
48
푓′′′(ℎ/2) +
ℎ4
384
푓′′′′(ℎ/2) + . . . .
Adding and multiplying by ℎ/2 we obtain
ℎ
2
(︀
푓(0) + 푓(ℎ)
)︀
= ℎ푓(ℎ/2) +
ℎ3
8
푓′′(ℎ/2) +
ℎ5
384
푓′′′′(ℎ/2) + . . . .
We now substitute for the first term on the right-hand-side using the midpoint
rule formula:
ℎ
2
(︀
푓(0) + 푓(ℎ)
)︀
=
(︂
퐼ℎ −
ℎ3
24
푓′′(ℎ/2) −
ℎ5
1920
푓′′′′(ℎ/2)
)︂
+
ℎ3
8
푓′′(ℎ/2) +
ℎ5
384
푓′′′′(ℎ/2) + . . . ,
and solving for 퐼ℎ, we find
퐼ℎ =
ℎ
2
(︀
푓(0) + 푓(ℎ)
)︀
−
ℎ3
12
푓′′(ℎ/2) −
ℎ5
480
푓′′′′(ℎ/2) + . . . . (6.4)

6.2. COMPOSITE RULES 37
6.1.3 Simpson’s rule
To obtain Simpson’s rule, we combine the midpoint and trapezoidal rule to
eliminate the error term proportional to ℎ3. Multiplying (6.3) by two and
adding to (6.4), we obtain
3퐼ℎ = ℎ
(︂
2푓(ℎ/2) +
1
2
(푓(0) + 푓(ℎ))
)︂
+ ℎ5
(︂
2
1920
−
1
480
)︂
푓′′′′(ℎ/2) + . . . ,
or
퐼ℎ =
ℎ
6
(︀
푓(0) + 4푓(ℎ/2) + 푓(ℎ)
)︀
−
ℎ5
2880
푓′′′′(ℎ/2) + . . . .
Usually, Simpson’s rule is written by considering the three consecutive points
0, ℎ and 2ℎ. Substituting ℎ → 2ℎ, we obtain the standard result
퐼2ℎ =
ℎ
3
(︀
푓(0) + 4푓(ℎ) + 푓(2ℎ)
)︀
−
ℎ5
90
푓′′′′(ℎ) + . . . . (6.5)
6.2 Composite rules
We now use our elementary formulas obtained for (6.2) to perform the integral
given by (6.1).
6.2.1 Trapezoidal rule
We suppose that the function 푓(푥) is known at the 푛 + 1 points labeled as
푥0, 푥1, . . . , 푥푛, with the endpoints given by 푥0 = 푎 and 푥푛 = 푏. Define
푓푖 = 푓(푥푖), ℎ푖 = 푥푖+1 − 푥푖.
Then the integral of (6.1) may be decomposed as
∫︁ 푏
푎
푓(푥)푑푥 =
푛Σ︁−1
푖=0
∫︁ 푥푖+1
푥푖
푓(푥)푑푥
=
푛Σ︁−1
푖=0
∫︁ ℎ푖
0
푓(푥푖 + 푠)푑푠,
where the last equality arises from the change-of-variables 푠 = 푥−푥푖. Applying
the trapezoidal rule to the integral, we have
∫︁ 푏
푎
푓(푥)푑푥 =
푛Σ︁−1
푖=0
ℎ푖
2
(푓푖 + 푓푖+1) . (6.6)
If the points are not evenly spaced, say because the data are experimental values,
then the ℎ푖 may differ for each value of 푖 and (6.6) is to be used directly.
However, if the points are evenly spaced, say because 푓(푥) can be computed,
we have ℎ푖 = ℎ, independent of 푖. We can then define
푥푖 = 푎 + 푖ℎ, 푖 = 0, 1, . . . , 푛;

and since the end point 푏 satisfies 푏 = 푎 + 푛ℎ, we have
ℎ =
푏 − 푎
푛
.
The composite trapezoidal rule for evenly space points then becomes
∫︁ 푏
푎
푓(푥)푑푥 =
ℎ
2
푛Σ︁−1
푖=0
(푓푖 + 푓푖+1)
=
ℎ
2
(푓0 + 2푓1 + · · · + 2푓푛−1 + 푓푛) . (6.7)
The first and last terms have a multiple of one; all other terms have a multiple
of two; and the entire sum is multiplied by ℎ/2.
6.2.2 Simpson’s rule
We here consider the composite Simpson’s rule for evenly space points. We
apply Simpson’s rule over intervals of 2ℎ, starting from 푎 and ending at 푏:
∫︁ 푏
푎
푓(푥)푑푥 =
ℎ
3
(푓0 + 4푓1 + 푓2) +
ℎ
3
(푓2 + 4푓3 + 푓4) + . . .
+
ℎ
3
(푓푛−2 + 4푓푛−1 + 푓푛) .
Note that 푛 must be even for this scheme to work. Combining terms, we have
∫︁ 푏
푎
푓(푥)푑푥 =
ℎ
3
(푓0 + 4푓1 + 2푓2 + 4푓3 + 2푓4 + · · · + 4푓푛−1 + 푓푛) .
The first and last terms have a multiple of one; the even indexed terms have a
multiple of 2; the odd indexed terms have a multiple of 4; and the entire sum is
multiplied by ℎ/3.
6.3 Local versus global error
Consider the elementary formula (6.4) for the trapezoidal rule, written in the
form ∫︁ ℎ
0
푓(푥)푑푥 =
ℎ
2
(︀
푓(0) + 푓(ℎ)
)︀
−
ℎ3
12
푓′′(휉),
where 휉 is some value satisfying 0 ≤ 휉 ≤ ℎ, and we have used Taylor’s theorem
with the mean-value form of the remainder. We can also represent the remainder
as
−
ℎ3
12
푓′′(휉) = O(ℎ3),
where O(ℎ3) signifies that when ℎ is small, halving of the grid spacing ℎ de-creases
the error in the elementary trapezoidal rule by a factor of eight. We call
the terms represented by O(ℎ3) the Local Error.

6.4. ADAPTIVE INTEGRATION 39
푎 푑 푐 푒 푏
Figure 6.1: Adaptive Simpson quadrature: Level 1.
More important is the Global Error which is obtained from the composite
formula (6.7) for the trapezoidal rule. Putting in the remainder terms, we have
∫︁ 푏
푎
푓(푥)푑푥 =
ℎ
2
(푓0 + 2푓1 + · · · + 2푓푛−1 + 푓푛) −
ℎ3
12
푛Σ︁−1
푖=0
푓′′(휉푖),
where 휉푖 are values satisfying 푥푖 ≤ 휉푖 ≤ 푥푖+1. The remainder can be rewritten
as
−
ℎ3
12
푛Σ︁−1
푖=0
푓′′(휉푖) = −
푛ℎ3
12
⟨︀
푓′′(휉푖)
⟩︀
,
where
⟨︀
푓′′(휉푖)
⟩︀
is the average value of all the 푓′′(휉푖)’s. Now,
푛 =
푏 − 푎
ℎ
,
so that the error term becomes
−
푛ℎ3
12
⟨︀
푓′′(휉푖)
⟩︀
= −
(푏 − 푎)ℎ2
12
⟨︀
푓′′(휉푖)
⟩︀
= O(ℎ2).
Therefore, the global error is O(ℎ2). That is, a halving of the grid spacing only
decreases the global error by a factor of four.
Similarly, Simpson’s rule has a local error of O(ℎ5) and a global error of
O(ℎ4).
6.4 Adaptive integration
The useful MATLAB function quad.m performs numerical integration using
adaptive Simpson quadrature. The idea is to let the computation itself decide
on the grid size required to achieve a certain level of accuracy. Moreover, the
grid size need not be the same over the entire region of integration.
We begin the adaptive integration at what is called Level 1. The uniformly
spaced points at which the function 푓(푥) is to be evaluated are shown in Fig.
6.1. The distance between the points 푎 and 푏 is taken to be 2ℎ, so that
ℎ =
푏 − 푎
2
.

Integration using Simpson’s rule (6.5) with grid size ℎ yields
퐼 =
ℎ
3
(︀
푓(푎) + 4푓(푐) + 푓(푏)
)︀
−
ℎ5
90
푓′′′′(휉),
where 휉 is some value satisfying 푎 ≤ 휉 ≤ 푏.
Integration using Simpson’s rule twice with grid size ℎ/2 yields
퐼 =
ℎ
6
(︀
푓(푎) + 4푓(푑) + 2푓(푐) + 4푓(푒) + 푓(푏)
)︀
−
(ℎ/2)5
90
푓′′′′(휉푙) −
(ℎ/2)5
90
푓′′′′(휉푟),
with 휉푙 and 휉푟 some values satisfying 푎 ≤ 휉푙 ≤ 푐 and 푐 ≤ 휉푟 ≤ 푏.
We now define
푆1 =
ℎ
3
(︀
푓(푎) + 4푓(푐) + 푓(푏)
)︀
,
푆2 =
ℎ
6
(︀
푓(푎) + 4푓(푑) + 2푓(푐) + 4푓(푒) + 푓(푏)
)︀
,
퐸1 = −
ℎ5
90
푓′′′′(휉),
퐸2 = −
ℎ5
25 · 90
(︀
푓′′′′(휉푙) + 푓′′′′(휉푟)
)︀
.
Now we ask whether 푆2 is accurate enough, or must we further refine the cal-culation
and go to Level 2? To answer this question, we make the simplifying
approximation that all of the fourth-order derivatives of 푓(푥) in the error terms
are equal; that is,
푓′′′′(휉) = 푓′′′′(휉푙) = 푓′′′′(휉푟) = 퐶.
Then
퐸1 = −
ℎ5
90
퐶,
퐸2 = −
ℎ5
24 · 90
퐶 =
1
16
퐸1.
Then since
푆1 + 퐸1 = 푆2 + 퐸2,
and
퐸1 = 16퐸2,
we have for our estimate for the error term 퐸2,
퐸2 =
1
15
(푆2 − 푆1).
Therefore, given some specific value of the tolerance tol, if
⃒⃒⃒⃒
1
15
⃒⃒⃒⃒
(푆2 − 푆1)
tol,
then we can accept 푆2 as 퐼. If the tolerance is not achieved for 퐼, then we
proceed to Level 2.
The computation at Level 2 further divides the integration interval from 푎
to 푏 into the two integration intervals 푎 to 푐 and 푐 to 푏, and proceeds with the

6.4. ADAPTIVE INTEGRATION 41
above procedure independently on both halves. Integration can be stopped on
either half provided the tolerance is less than tol/2 (since the sum of both errors
must be less than tol). Otherwise, either half can proceed to Level 3, and so on.
As a side note, the two values of 퐼 given above (for integration with step
size ℎ and ℎ/2) can be combined to give a more accurate value for I given by
퐼 =
16푆2 − 푆1
15
+ O(ℎ7),
where the error terms of O(ℎ5) approximately cancel. This free lunch, so to
speak, is called Richardson’s extrapolation.

Chapter 7
Ordinary differential
equations
We now discuss the numerical solution of ordinary differential equations. These
include the initial value problem, the boundary value problem, and the eigen-value
problem. Before proceeding to the development of numerical methods, we
review the analytical solution of some classic equations.
7.1 Examples of analytical solutions
7.1.1 Initial value problem
One classic initial value problem is the 푅퐶 circuit. With 푅 the resistor and 퐶
the capacitor, the differential equation for the charge 푞 on the capacitor is given
by
푅
푑푞
푑푡
+
푞
퐶
= 0. (7.1)
If we consider the physical problem of a charged capacitor connected in a closed
circuit to a resistor, then the initial condition is 푞(0) = 푞0, where 푞0 is the initial
charge on the capacitor.
The differential equation (7.1) is separable, and separating and integrating
from time 푡 = 0 to 푡 yields
∫︁ 푞
푞0
푑푞
푞
= −
1
푅퐶
∫︁ 푡
0
푑푡,
which can be integrated and solved for 푞 = 푞(푡):
푞(푡) = 푞0푒−푡/푅퐶.
The classic second-order initial value problem is the 푅퐿퐶 circuit, with dif-ferential
equation
퐿
푑2푞
푑푡2 + 푅
푑푞
푑푡
+
푞
퐶
= 0.
Here, a charged capacitor is connected to a closed circuit, and the initial condi-tions
satisfy
푞(0) = 푞0,
푑푞
푑푡
(0) = 0.
43

44 CHAPTER 7. ORDINARY DIFFERENTIAL EQUATIONS
The solution is obtained for the second-order equation by the ansatz
푞(푡) = 푒푟푡,
which results in the following so-called characteristic equation for 푟:
퐿푟2 + 푅푟 +
1
퐶
= 0.
If the two solutions for 푟 are distinct and real, then the two found exponential
solutions can be multiplied by constants and added to form a general solution.
The constants can then be determined by requiring the general solution to satisfy
the two initial conditions. If the roots of the characteristic equation are complex
or degenerate, a general solution to the differential equation can also be found.
7.1.2 Boundary value problems
The dimensionless equation for the temperature 푦 = 푦(푥) along a linear heat-conducting
rod of length unity, and with an applied external heat source 푓(푥),
is given by the differential equation
−
푑2푦
푑푥2 = 푓(푥), (7.2)
with 0 ≤ 푥 ≤ 1. Boundary conditions are usually prescribed at the end points of
the rod, and here we assume that the temperature at both ends are maintained
at zero so that
푦(0) = 0, 푦(1) = 0.
The assignment of boundary conditions at two separate points is called a two-point
boundary value problem, in contrast to the initial value problem where the
boundary conditions are prescribed at only a single point. Two-point boundary
value problems typically require a more sophisticated algorithm for a numerical
solution than initial value problems.
Here, the solution of (7.2) can proceed by integration once 푓(푥) is specified.
We assume that
푓(푥) = 푥(1 − 푥),
so that the maximum of the heat source occurs in the center of the rod, and
goes to zero at the ends.
The differential equation can then be written as
푑2푦
푑푥2 = −푥(1 − 푥).
The first integration results in
푑푦
푑푥
=
∫︁
(푥2 − 푥)푑푥
=
푥3
3
−
푥2
2
+ 푐1,

7.1. EXAMPLES OF ANALYTICAL SOLUTIONS 45
where 푐1 is the first integration constant. Integrating again,
푦(푥) =
∫︁ (︂
푥3
3
−
푥2
2
+ 푐1
)︂
푑푥
=
푥4
12
−
푥3
6
+ 푐1푥 + 푐2,
where 푐2 is the second integration constant. The two integration constants are
determined by the boundary conditions. At 푥 = 0, we have
0 = 푐2,
and at 푥 = 1, we have
0 =
1
12
−
1
6
+ 푐1,
so that 푐1 = 1/12. Our solution is therefore
푦(푥) =
푥4
12
−
푥3
6
+
푥
12
=
1
12
푥(1 − 푥)(1 + 푥 − 푥2).
The temperature of the rod is maximum at 푥 = 1/2 and goes smoothly to zero
at the ends.
7.1.3 Eigenvalue problem
The classic eigenvalue problem obtained by solving the wave equation by sepa-ration
of variables is given by
푑2푦
푑푥2 + 휆2푦 = 0,
with the two-point boundary conditions 푦(0) = 0 and 푦(1) = 0. Notice that
푦(푥) = 0 satisfies both the differential equation and the boundary conditions.
Other nonzero solutions for 푦 = 푦(푥) are possible only for certain discrete values
of 휆. These values of 휆 are called the eigenvalues of the differential equation.
We proceed by first finding the general solution to the differential equation.
It is easy to see that this solution is
푦(푥) = 퐴cos 휆푥 + 퐵 sin 휆푥.
Imposing the first boundary condition at 푥 = 0, we obtain
퐴 = 0.
The second boundary condition at 푥 = 1 results in
퐵 sin 휆 = 0.
Since we are searching for a solution where 푦 = 푦(푥) is not identically zero, we
must have
휆 = 휋, 2휋, 3휋, . . . .

The corresponding negative values of 휆 are also solutions, but their inclusion
only changes the corresponding values of the unknown 퐵 constant. A linear
superposition of all the solutions results in the general solution
푦(푥) =
∞Σ︁
푛=1
퐵푛 sin 푛휋푥.
For each eigenvalue 푛휋, we say there is a corresponding eigenfunction sin 푛휋푥.
When the differential equation can not be solved analytically, a numerical
method should be able to solve for both the eigenvalues and eigenfunctions.
7.2 Numerical methods: initial value problem
We begin with the simple Euler method, then discuss the more sophisticated
Runge-Kutta methods, and conclude with the Runge-Kutta-Fehlberg method,
as implemented in the MATLAB function ode45.m. Our differential equations
are for 푥 = 푥(푡), where the time 푡 is the independent variable, and we will make
use of the notation 푥˙ = 푑푥/푑푡. This notation is still widely used by physicists
and descends directly from the notation originally used by Newton.
7.2.1 Euler method
The Euler method is the most straightforward method to integrate a differential
equation. Consider the first-order differential equation
푥˙ = 푓(푡, 푥), (7.3)
with the initial condition 푥(0) = 푥0. Define 푡푛 = 푛Δ푡 and 푥푛 = 푥(푡푛). A Taylor
series expansion of 푥푛+1 results in
푥푛+1 = 푥(푡푛 + Δ푡)
= 푥(푡푛) + Δ푡푥˙ (푡푛) + O(Δ푡2)
= 푥(푡푛) + Δ푡푓 (푡푛, 푥푛) + O(Δ푡2).
The Euler Method is therefore written as
푥푛+1 = 푥(푡푛) + Δ푡푓 (푡푛, 푥푛).
We say that the Euler method steps forward in time using a time-step Δ푡,
starting from the initial value 푥0 = 푥(0). The local error of the Euler Method
is O(Δ푡2). The global error, however, incurred when integrating to a time 푇, is
a factor of 1/Δ푡 larger and is given by O(Δ푡). It is therefore customary to call
the Euler Method a first-order method.
7.2.2 Modified Euler method
This method is of a type that is called a predictor-corrector method. It is also
the first of what are Runge-Kutta methods. As before, we want to solve (7.3).
The idea is to average the value of 푥˙ at the beginning and end of the time step.
That is, we would like to modify the Euler method and write
푥푛+1 = 푥푛 +
1
2
Δ푡
(︀
푓(푡푛, 푥푛) + 푓(푡푛 + Δ푡, 푥푛+1)
)︀
.

7.2. NUMERICAL METHODS: INITIAL VALUE PROBLEM 47
The obvious problem with this formula is that the unknown value 푥푛+1 appears
on the right-hand-side. We can, however, estimate this value, in what is called
the predictor step. For the predictor step, we use the Euler method to find
푥푝
푛+1 = 푥푛 + Δ푡푓 (푡푛, 푥푛).
The corrector step then becomes
푥푛+1 = 푥푛 +
1
2
Δ푡
(︀
푓(푡푛, 푥푛) + 푓(푡푛 + Δ푡, 푥푝
푛+1)
)︀
.
The Modified Euler Method can be rewritten in the following form that we will
later identify as a Runge-Kutta method:
푘1 = Δ푡푓 (푡푛, 푥푛),
푘2 = Δ푡푓 (푡푛 + Δ푡, 푥푛 + 푘1),
푥푛+1 = 푥푛 +
1
2
(푘1 + 푘2).
(7.4)
7.2.3 Second-order Runge-Kutta methods
We now derive all second-order Runge-Kutta methods. Higher-order methods
can be similarly derived, but require substantially more algebra.
We consider the differential equation given by (7.3). A general second-order
Runge-Kutta method may be written in the form
푘2 = Δ푡푓 (푡푛 + 훼Δ푡, 푥푛 + 훽푘1),
푥푛+1 = 푥푛 + 푎푘1 + 푏푘2,
(7.5)
with 훼, 훽, 푎 and 푏 constants that define the particular second-order Runge-
Kutta method. These constants are to be constrained by setting the local error
of the second-order Runge-Kutta method to be O(Δ푡3). Intuitively, we might
guess that two of the constraints will be 푎 + 푏 = 1 and 훼 = 훽.
We compute the Taylor series of 푥푛+1 directly, and from the Runge-Kutta
method, and require them to be the same to order Δ푡2. First, we compute the
Taylor series of 푥푛+1. We have
푥푛+1 = 푥(푡푛 + Δ푡)
= 푥(푡푛) + Δ푡푥˙ (푡푛) +
1
2
(Δ푡)2¨푥(푡푛) + O(Δ푡3).
Now,
푥˙ (푡푛) = 푓(푡푛, 푥푛).
The second derivative is more complicated and requires partial derivatives. We
have
¨푥(푡푛) =
푑
푑푡
푓(푡, 푥(푡))
]︂
푡=푡푛
= 푓푡(푡푛, 푥푛) + 푥˙ (푡푛)푓푥(푡푛, 푥푛)
= 푓푡(푡푛, 푥푛) + 푓(푡푛, 푥푛)푓푥(푡푛, 푥푛).

Therefore,
푥푛+1 = 푥푛 + Δ푡푓 (푡푛, 푥푛) +
1
2
(Δ푡)2 (︀
푓푡(푡푛, 푥푛) + 푓(푡푛, 푥푛)푓푥(푡푛, 푥푛)
)︀
. (7.6)
Second, we compute 푥푛+1 from the Runge-Kutta method given by (7.5).
Substituting in 푘1 and 푘2, we have
푥푛+1 = 푥푛 + 푎Δ푡푓 (푡푛, 푥푛) + 푏Δ푡푓
(︀
푡푛 + 훼Δ푡, 푥푛 + 훽Δ푡푓 (푡푛, 푥푛)
)︀
.
We Taylor series expand using
푓
(︀
푡푛 + 훼Δ푡, 푥푛 + 훽Δ푡푓 (푡푛, 푥푛)
)︀
= 푓(푡푛, 푥푛) + 훼Δ푡푓푡(푡푛, 푥푛) + 훽Δ푡푓 (푡푛, 푥푛)푓푥(푡푛, 푥푛) + O(Δ푡2).
The Runge-Kutta formula is therefore
푥푛+1 = 푥푛 + (푎 + 푏)Δ푡푓 (푡푛, 푥푛)
+ (Δ푡)2(︀
훼푏푓푡(푡푛, 푥푛) + 훽푏푓(푡푛, 푥푛)푓푥(푡푛, 푥푛)
)︀
+ O(Δ푡3). (7.7)
Comparing (7.6) and (7.7), we find
푎 + 푏 = 1,
훼푏 = 1/2,
훽푏 = 1/2.
There are three equations for four parameters, and there exists a family of
second-order Runge-Kutta methods.
The Modified Euler Method given by (7.4) corresponds to 훼 = 훽 = 1 and
푎 = 푏 = 1/2. Another second-order Runge-Kutta method, called the Midpoint
Method, corresponds to 훼 = 훽 = 1/2, 푎 = 0 and 푏 = 1. This method is written
as
푘2 = Δ푡푓
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘1
)︂
,
푥푛+1 = 푥푛 + 푘2.
7.2.4 Higher-order Runge-Kutta methods
The general second-order Runge-Kutta method was given by (7.5). The general
form of the third-order method is given by
푘2 = Δ푡푓 (푡푛 + 훼Δ푡, 푥푛 + 훽푘1),
푘3 = Δ푡푓 (푡푛 + 훾Δ푡, 푥푛 + 훿푘1 + 휖푘2),
푥푛+1 = 푥푛 + 푎푘1 + 푏푘2 + 푐푘3.
The following constraints on the constants can be guessed: 훼 = 훽, 훾 = 훿 + 휖,
and 푎 + 푏 + 푐 = 1. Remaining constraints need to be derived.

7.2. NUMERICAL METHODS: INITIAL VALUE PROBLEM 49
The fourth-order method has a 푘1, 푘2, 푘3 and 푘4. The fifth-order method
requires up to 푘6. The table below gives the order of the method and the number
of stages required.
order 2 3 4 5 6 7 8
minimum # stages 2 3 4 6 7 9 11
Because of the jump in the number of stages required between the fourth-order
and fifth-order method, the fourth-order Runge-Kutta method has some
appeal. The general fourth-order method starts with 13 constants, and one then
finds 11 constraints. A particularly simple fourth-order method that has been
widely used is given by
푘2 = Δ푡푓
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘1
)︂
,
푘3 = Δ푡푓
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘2
)︂
,
푘4 = Δ푡푓 (푡푛 + Δ푡, 푥푛 + 푘3) ,
푥푛+1 = 푥푛 +
1
6
(푘1 + 2푘2 + 2푘3 + 푘4) .
7.2.5 Adaptive Runge-Kutta Methods
As in adaptive integration, it is useful to devise an ode integrator that au-tomatically
finds the appropriate Δ푡. The Dormand-Prince Method, which is
implemented in MATLAB’s ode45.m, finds the appropriate step size by compar-ing
the results of a fifth-order and fourth-order method. It requires six function
evaluations per time step, and constructs both a fifth-order and a fourth-order
method from these function evaluations.
Suppose the fifth-order method finds 푥푛+1 with local error O(Δ푡6), and the
fourth-order method finds 푥′
푛+1 with local error O(Δ푡5). Let 휀 be the desired
error tolerance of the method, and let 푒 be the actual error. We can estimate 푒
from the difference between the fifth- and fourth-order methods; that is,
푒 = |푥푛+1 − 푥′
푛+1|.
Now 푒 is of O(Δ푡5), where Δ푡 is the step size taken. Let Δ휏 be the estimated
step size required to get the desired error 휀. Then we have
푒/휀 = (Δ푡)5/(Δ휏 )5,
or solving for Δ휏 ,
Δ휏 = Δ푡
(︁휀
푒
)︁1/5
.
On the one hand, if 푒 휀, then we accept 푥푛+1 and do the next time step
using the larger value of Δ휏 . On the other hand, if 푒 휀, then we reject
the integration step and redo the time step using the smaller value of Δ휏 . In
practice, one usually increases the time step slightly less and decreases the time
step slightly more to prevent the waste of too many failed time steps.

7.2.6 System of differential equations
Our numerical methods can be easily adapted to solve higher-order differential
equations, or equivalently, a system of differential equations. First, we show how
a second-order differential equation can be reduced to two first-order equations.
Consider
푥¨ = 푓(푡, 푥, 푥˙ ).
This second-order equation can be rewritten as two first-order equations by
defining 푢 = 푥˙ . We then have the system
푥˙ = 푢,
푢˙ = 푓(푡, 푥, 푢).
This trick also works for higher-order equation. For another example, the third-order
equation
...
푥 = 푓(푡, 푥, 푥˙ , 푥¨),
can be written as
푥˙ = 푢,
푢˙ = 푣,
푣˙ = 푓(푡, 푥, 푢, 푣).
Now, we show how to generalize Runge-Kutta methods to a system of dif-ferential
equations. As an example, consider the following system of two odes,
푥˙ = 푓(푡, 푥, 푦),
푦˙ = 푔(푡, 푥, 푦),
with the initial conditions 푥(0) = 푥0 and 푦(0) = 푦0. The generalization of the

7.3. NUMERICAL METHODS: BOUNDARY VALUE PROBLEM 51
commonly used fourth-order Runge-Kutta method would be
푘1 = Δ푡푓 (푡푛, 푥푛, 푦푛),
푙1 = Δ푡푔(푡푛, 푥푛, 푦푛),
푘2 = Δ푡푓
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘1, 푦푛 +
1
2
푙1
)︂
,
푙2 = Δ푡푔
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘1, 푦푛 +
1
2
푙1
)︂
,
푘3 = Δ푡푓
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘2, 푦푛 +
1
2
푙2
)︂
,
푙3 = Δ푡푔
(︂
푡푛 +
1
2
Δ푡, 푥푛 +
1
2
푘2, 푦푛 +
1
2
푙2
)︂
,
푘4 = Δ푡푓 (푡푛 + Δ푡, 푥푛 + 푘3, 푦푛 + 푙3) ,
푙4 = Δ푡푔 (푡푛 + Δ푡, 푥푛 + 푘3, 푦푛 + 푙3) ,
푥푛+1 = 푥푛 +
1
6
(푘1 + 2푘2 + 2푘3 + 푘4) ,
푦푛+1 = 푦푛 +
1
6
(푙1 + 2푙2 + 2푙3 + 푙4) .
7.3 Numerical methods: boundary value prob-lem
7.3.1 Finite difference method
We consider first the differential equation
−
푑2푦
푑푥2 = 푓(푥), 0 ≤ 푥 ≤ 1, (7.8)
with two-point boundary conditions
푦(0) = 퐴, 푦(1) = 퐵.
Equation (7.8) can be solved by quadrature, but here we will demonstrate a
numerical solution using a finite difference method.
We begin by discussing how to numerically approximate derivatives. Con-sider
the Taylor series approximation for 푦(푥 + ℎ) and 푦(푥 − ℎ), given by
푦(푥 + ℎ) = 푦(푥) + ℎ푦′(푥) +
1
2
ℎ2푦′′(푥) +
1
6
ℎ3푦′′′(푥) +
1
24
ℎ4푦′′′′(푥) + . . . ,
푦(푥 − ℎ) = 푦(푥) − ℎ푦′(푥) +
1
2
ℎ2푦′′(푥) −
1
6
ℎ3푦′′′(푥) +
1
24
ℎ4푦′′′′(푥) + . . . .

The standard definitions of the derivatives give the first-order approximations
푦′(푥) =
푦(푥 + ℎ) − 푦(푥)
ℎ
+ O(ℎ),
푦′(푥) =
푦(푥) − 푦(푥 − ℎ)
ℎ
+ O(ℎ).
The more widely-used second-order approximation is called the central differ-ence
approximation and is given by
푦′(푥) =
푦(푥 + ℎ) − 푦(푥 − ℎ)
2ℎ
+ O(ℎ2).
The finite difference approximation to the second derivative can be found from
considering
푦(푥 + ℎ) + 푦(푥 − ℎ) = 2푦(푥) + ℎ2푦′′(푥) +
1
12
ℎ4푦′′′′(푥) + . . . ,
from which we find
푦′′(푥) =
푦(푥 + ℎ) − 2푦(푥) + 푦(푥 − ℎ)
ℎ2 + O(ℎ2).
Sometimes a second-order method is required for 푥 on the boundaries of the
domain. For a boundary point on the left, a second-order forward difference
method requires the additional Taylor series
푦(푥 + 2ℎ) = 푦(푥) + 2ℎ푦′(푥) + 2ℎ2푦′′(푥) +
4
3
ℎ3푦′′′(푥) + . . . .
We combine the Taylor series for 푦(푥 + ℎ) and 푦(푥 + 2ℎ) to eliminate the term
proportional to ℎ2:
푦(푥 + 2ℎ) − 4푦(푥 + ℎ) = −3푦(푥) − 2ℎ푦′(푥) + O(h3).
Therefore,
푦′(푥) =
−3푦(푥) + 4푦(푥 + ℎ) − 푦(푥 + 2ℎ)
2ℎ
+ O(ℎ2).
For a boundary point on the right, we send ℎ → −ℎ to find
푦′(푥) =
3푦(푥) − 4푦(푥 − ℎ) + 푦(푥 − 2ℎ)
2ℎ
+ O(ℎ2).
We now write a finite difference scheme to solve (7.8). We discretize 푥 by
defining 푥푖 = 푖ℎ, 푖 = 0, 1, . . . , 푛 + 1. Since 푥푛+1 = 1, we have ℎ = 1/(푛 + 1).
The functions 푦(푥) and 푓(푥) are discretized as 푦푖 = 푦(푥푖) and 푓푖 = 푓(푥푖). The
second-order differential equation (7.8) then becomes for the interior points of
the domain
−푦푖−1 + 2푦푖 − 푦푖+1 = ℎ2푓푖, 푖 = 1, 2, . . . 푛,
with the boundary conditions 푦0 = 퐴 and 푦푛+1 = 퐵. We therefore have a linear
system of equations to solve. The first and 푛th equation contain the boundary
conditions and are given by
2푦1 − 푦2 = ℎ2푓1 + 퐴,
−푦푛−1 + 2푦푛 = ℎ2푓푛 + 퐵.

7.3. NUMERICAL METHODS: BOUNDARY VALUE PROBLEM 53
The second and third equations, etc., are
−푦1 + 2푦2 − 푦3 = ℎ2푓2,
−푦2 + 2푦3 − 푦4 = ℎ2푓3,
. . .
In matrix form, we have
⎛
⎜⎜⎜⎜⎜⎜⎜⎝
⎞
2 −1 0 0 . . . 0 0 0
−1 2 −1 0 . . . 0 0 0
0 −1 2 −1 . . . 0 0 0
...
...
...
...
. . .
...
...
...
⎟⎟⎟⎟⎟⎟⎟⎠
0 0 0 0 . . . −1 2 −1
0 0 0 0 . . . 0 −1 2
⎛
⎜⎜⎜⎜⎜⎜⎜⎝
푦1
푦2
푦3
...
푦푛−1
푦푛
⎞
⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛
⎜⎜⎜⎜⎜⎜⎜⎝
ℎ2푓1 + 퐴
ℎ2푓2
ℎ2푓3
...
ℎ2푓푛−1
ℎ2푓푛 + 퐵
⎞
⎟⎟⎟⎟⎟⎟⎟⎠
.
The matrix is tridiagonal, and a numerical solution by Guassian elimination
can be done quickly. The matrix itself is easily constructed using the MATLAB
function diag.m and ones.m. As excerpted from the MATLAB help page, the
function call ones(m,n) returns an m-by-n matrix of ones, and the function call
diag(v,k), when v is a vector with n components, is a square matrix of order
n+abs(k) with the elements of v on the k-th diagonal: 푘 = 0 is the main diag-onal,
푘 0 is above the main diagonal and 푘 0 is below the main diagonal.
The 푛 × 푛 matrix above can be constructed by the MATLAB code
M=diag(-ones(n-1,1),-1)+diag(2*ones(n,1),0)+diag(-ones(n-1,1),1); .
The right-hand-side, provided f is a given n-by-1 vector, can be constructed
by the MATLAB code
b=h^2*f; b(1)=b(1)+A; b(n)=b(n)+B;
and the solution for u is given by the MATLAB code
y=M∖b;
7.3.2 Shooting method
The finite difference method can solve linear odes. For a general ode of the form
푑2푦
푑푥2 = 푓(푥, 푦, 푑푦/푑푥),
with 푦(0) = 퐴 and 푦(1) = 퐵, we use a shooting method. First, we formulate
the ode as an initial value problem. We have
푑푦
푑푥
= 푧,
푑푧
푑푥
= 푓(푥, 푦, 푧).

The initial condition 푦(0) = 퐴 is known, but the second initial condition 푧(0) = 푏
is unknown. Our goal is to determine 푏 such that 푦(1) = 퐵.
In fact, this is a root-finding problem for an appropriately defined function.
We define the function 퐹 = 퐹(푏) such that
퐹(푏) = 푦(1) − 퐵.
In other words, 퐹(푏) is the difference between the value of 푦(1) obtained from
integrating the differential equations using the initial condition 푧(0) = 푏, and
퐵. Our root-finding routine will want to solve 퐹(푏) = 0. (The method is called
shooting because the slope of the solution curve for 푦 = 푦(푥) at 푥 = 0 is given
by 푏, and the solution hits the value 푦(1) at 푥 = 1. This looks like pointing a
gun and trying to shoot the target, which is 퐵.)
To determine the value of 푏 that solves 퐹(푏) = 0, we iterate using the Secant
method, given by
푏푛+1 = 푏푛 − 퐹(푏푛)
푏푛 − 푏푛−1
퐹(푏푛) − 퐹(푏푛−1)
.
We need to start with two initial guesses for 푏, solving the ode for the two
corresponding values of 푦(1). Then the Secant Method will give us the next value
of 푏 to try, and we iterate until |푦(1) − 퐵| tol, where tol is some specified
tolerance for the error.
7.4 Numerical methods: eigenvalue problem
For illustrative purposes, we develop our numerical methods for what is perhaps
the simplest eigenvalue ode. With 푦 = 푦(푥) and 0 ≤ 푥 ≤ 1, this simple ode is
given by
푦′′ + 휆2푦 = 0. (7.9)
To solve (7.9) numerically, we will develop both a finite difference method and
a shooting method. Furthermore, we will show how to solve (7.9) with homo-geneous
boundary conditions on either the function 푦 or its derivative 푦′.
7.4.1 Finite difference method
We first consider solving (7.9) with the homogeneous boundary conditions 푦(0) =
푦(1) = 0. In this case, we have already shown that the eigenvalues of (7.9) are
given by 휆 = 휋, 2휋, 3휋, . . . .
With 푛 interior points, we have 푥푖 = 푖ℎ for 푖 = 0, . . . , 푛 + 1, and ℎ =
1/(푛 + 1). Using the centered-finite-difference approximation for the second
derivative, (7.9) becomes
푦푖−1 − 2푦푖 + 푦푖+1 = −ℎ2휆2푦푖. (7.10)
Applying the boundary conditions 푦0 = 푦푛+1 = 0, the first equation with 푖 = 1,
and the last equation with 푖 = 푛, are given by
−2푦1 + 푦2 = −ℎ2휆2푦1,
푦푛−1 − 2푦푛 = −ℎ2휆2푦푛.

7.4. NUMERICAL METHODS: EIGENVALUE PROBLEM 55
The remaining 푛 − 2 equations are given by (7.10) for 푖 = 2, . . . , 푛 − 1.
It is of interest to see how the solution develops with increasing 푛. The
smallest possible value is 푛 = 1, corresponding to a single interior point, and
since ℎ = 1/2 we have
−2푦1 = −
1
4
휆2푦1,
so that 휆2 = 8, or 휆 = 2
√
2 = 2.8284. This is to be compared to the first
eigenvalue which is 휆 = 휋. When 푛 = 2, we have ℎ = 1/3, and the resulting
two equations written in matrix form are given by
(︂
−2 1
)︂(︂
1 −2
푦1
푦2
)︂
= −
1
9
휆2
(︂
푦1
푦2
)︂
.
This is a matrix eigenvalue problem with the eigenvalue given by 휇 = −휆2/9.
The solution for 휇 is arrived at by solving
det
(︂
−2 − 휇 1
)︂
1 −2 − 휇
= 0,
with resulting quadratic equation
(2 + 휇)2 − 1 = 0.
√
−휇, we have 휆 = 3, 3
The solutions are 휇 = −1,−3, and since 휆 = 3
√
3 =
5.1962. These two eigenvalues serve as rough approximations to the first two
eigenvalues 휋 and 2휋.
With A an 푛-by-푛 matrix, the MATLAB variable mu=eig(A) is a vector
containing the 푛 eigenvalues of the matrix A. The built-in function eig.m can
therefore be used to find the eigenvalues. With 푛 grid points, the smaller eigen-values
will converge more rapidly than the larger ones.
We can also consider boundary conditions on the derivative, or mixed bound-ary
conditions. For example, consider the mixed boundary conditions given by
푦(0) = 0 and 푦′(1) = 0. The eigenvalues of (7.9) can then be determined
analytically to be 휆푖 = (푖 − 1/2)휋, with 푖 a natural number.
The difficulty we now face is how to implement a boundary condition on
the derivative. Our computation of 푦′′ uses a second-order method, and we
would like the computation of the first derivative to also be second order. The
condition 푦′(1) = 0 occurs on the right-most boundary, and we can make use of
the second-order backward-difference approximation to the derivative that we
have previously derived. This finite-difference approximation for 푦′(1) can be
written as
푦′ 푛+1 =
3푦푛+1 − 4푦푛 + 푦푛−1
2ℎ
. (7.11)
Now, the 푛th finite-difference equation was given by
푦푛−1 − 2푦푛 + 푦푛+1 = −ℎ2푦푛,
and we now replace the value 푦푛+1 using (7.11); that is,
푦푛+1 =
1
3
(︀
2ℎ푦′푛
+1 + 4푦푛 − 푦푛−1
)︀
.

Numerical methods by Jeffrey R. Chasnov

Numerical methods by Jeffrey R. Chasnov

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