FP1: Chapter 2 Numerical Solutions of Equations

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FP1: Chapter 2 Numerical
Solutions of Equations
Dr J Frost ([email protected])
Last modified: 2nd January 2014
Iterative Methods
Suppose we wanted to find solutions to x = cos(x).
There is in fact no way to express the solution in an ‘exact’ way, i.e.
involving sums, divisions, roots, logs, trigonometric function, etc.
We instead have to use numerical methods to approximate the solution.
Iterative Methods
The principle of iterative methods is that we start with some initial approximation
of the solution, and ‘iteratively’ repeat some process to gradually get closer to the
true solution.
This is a method you will see in C3:
Suppose we start (by observing the
graph) with an approximation of
x0 = 0.5
We could use the iterative formula:
xn+1 = cos(xn)
You could do this on a calculator using:
[0.5] [=]
[cos] [ANS]
[=] [=] [=] [=] [=] ...
x = 0.739085...
?
Iterative Methods
There are different numerical methods we could use.
Why have different methods?
1
Some converge to the solution more quickly than others.
2
Some methods may not converge at all for certain equations/initial
choice of x, either diverging, or oscillating between values.
For FP1 we will be exploring 3 root-finding algorithms:
a. Interval Bisection
b. Linear Interpolation
c. Newton-Rhapson Process
These will all be used to find the roots of functions, i.e. the x for which f(x) = 0.
f(x) = 0
Root-finding algorithms find the root of a function!
Just simply put your equation into the form f(x) = 0 if not already so.
We want to solve...
Use f(x) =
x = cos(x)
f(x) = x – cos(x)
?
x2 = 2
f(x) = x2 – 2?
x = x3 + 3
f(x) = x3 – x?+ 3
x2 = 2x – 6
f(x) = x2 – 2x
?+6
Approach 1: Interval Bisection
This approach starts with an interval for which f(x) changes sign, then halves
this interval at each iteration. This is loosely an approach you used at GCSE.
Suppose we know the root lies
in the interval [a, b]
We want to narrow this interval.
a
a+b
2
a+b a+b
2
2
So try halfway.
We find f((a+b)/2)
is positive, so we
replace b with
(a+b)/2.
b
x
And repeat... 
Example
Using the initial interval [2,3], find the positive root to the equation x2 = 7 to 2dp.
f(x) =
x2
-7
Bro Tip: Keeping a table like this helps
you easily keep track of your bounds.
a
b
(a+b)/2
f((a+b)/2)
2
3
2.5
-0.75
2.5
3
2.75
0.5625
2.5
2.75
2.625
-0.109
2.625
2.75
0.223
2.625
2.6875
2.6875
?
? 2.65625
0.0557
...
2.64453125
2.646484375
2.645507813
We can stop at this point because:
Both the new bounds will be 2.65 to 2dp, so we know
?
the solution must be 2.65 to 2dp.
Exam Question
a
f(2) = -1, f(2.5) = 3.4062
(M1)
Sign change (and f(x) is continuous),
therefore a root α
?
exists between x = 2 and x = 2.5 (A1)
b
f(2.25) = 0.673828125
f(2.125) = -0.27526855
?
Therefore 2.125  α  2.25
(B1)
(M1)
(A1)
Bro Tip:
This is an incredibly
common question,
so know your mark
scheme here!
Exercises
To Do
Analysis of Interval Bisection
(Note that none of this bit will be in an exam)
Failure Analysis:
Guaranteed to converge to a root provided that for the initial interval [a,b],
f(a) and f(b) have opposite signs, and f(x) is continuous.
Other Comments:
Another advantage: Simple to carry out.
Rate of Convergence:
Horribly slow in terms of crawling very slowly towards the root. The error halves
each time – we say this is linear convergence.
Therefore in practice Interval Bisection tends not to be used.
Approach 2: Linear Interpolation
Linear Interpolation builds on the method of Interval Bisection by choosing a
point (generally) better than the midpoint of the bounds.
a
α
c
b
x
Initially the bound for our root is [a,b].
We establish c using linear interpolation, see
that f(c) is +ve, and hence adjust our interval
to [a,c] and repeat.
Approach 2: Linear Interpolation
Show that the equation x3 + 5x – 10 = 0 has a root in the interval [1,2]. Using linear
interpolation, find this root to 1 decimal place. (Hint: use similar triangles)
f(1) = -4,
f(2) = 8
(2 – α1)/(α1 – 1) = 8/4
Solving a1 = 1.33333
f(1.333...) = -0.96296. Since negative,
Make new interval [1.333..., 2]
?
Eventually we find a3 = 1.4196.
We can check 1.4 is correct to 1dp by
looking at f(1.35) and f(1.45). Change of
sign means root root lies in [1.35, 1.45],
i.e. 1.4 is correct to 1dp.
1
α1
2
x
Exam Question
?
Exercises
To Do
Analysis of Linear Interpolation
(Note that none of this bit will be in an exam)
Failure Analysis:
As with Interval Bisection, guaranteed to converge to a root provided that for the
initial interval [a,b], f(a) and f(b) have opposite signs, and f(x) is continuous.
Rate of Convergence:
The error of the approximation after each iteration is dependent on ‘curvy’ the line
is: as you might expect, the flatter the curve is, the more it approximates a straight
line, and hence the better linear interpolation will be (which assumes a straight
line in the region).
The rate of convergence is high when the second derivative at the root is small (i.e.
the gradient is not changing very much).
Approach 3: The Newton-Raphson Process
(also known as Newton’s method)
Suppose we start with an
approximation of the root, x0.
Clearly this is well off the mark.
y
A seemingly sensible thing to do
is to follow the direction of the
line, i.e. use the gradient of the
tangent.
We can keep repeating this
process to (hopefully) get
increasingly accurate
approximations.
Can you come up with a formula
for xn+1 in terms of xn?
x2
x1
x0
x
Approach 3: The Newton-Raphson Process
y
Formula:
Using C1 coordinate geometry:
y – f(xn) = f’(xn)(x – xn)
But we’re interested when
x = xn+1 and y = 0
?
-f(xn) = f’(xn)(xn+1 – xn)
which gives:
Newton-Raphson Process:
xn+1 = xn - f(xn)/f’(xn)
xn+1
xn
x
Approach 3: The Newton-Raphson Process
Demo (Courtesy of Wikipedia)
Example
Returning to our original example: x = cos(x), say letting x0 = 0.5
Let f(x) = x – cos(x)
f’(x) = 1 + sin(x)
f(0.5) = 0.5 – cos(0.5) = -0.3775825
f’(0.5) = 1 + sin(0.5) = 1.4794255
?
x1 = 0.5 – (-0.3775825 / 1.4794255)
= 0.7552224171
x2 = 0.7391412
x3 = 0.7390851
After merely three iterations, our approximation is accurate
to 7 decimal places. Holy smokes Batman!
Bro Tip: To perform
iterations quickly, do the
following on your
calculator:
[0.5] [=]
[ANS] – (ANS – cos(ANS))/(1
+ sin(ANS))
Then spam [=].
Quickfire Questions
Using Newton’s method, state the recurrence relation for the following functions.
f(x) = x3 - 2
xn+1 = xn – (xn3 – 2)/3xn2
f(x) = √x + x - 2
-0.5 + 1)
xn+1 = xn – (√xn + xn - 2)/(0.5x
?
n
f(x) = x2 – x – 1
xn+1 = xn – (xn2 – xn – 1?)/(2xn – 1)
Exam Question
June 2013 (Retracted)
f’(x) = 2x3 – 3x2 + 1
f’(-1.5) = -12.5
f(-1.5) = 1.40625
?
1 = -1.5 – 1.40625/(-12.5)
= -1.3875
Exercises
To do.
Really Nice Application #1
Find the square root of 3.
Using the C3 method of putting
equation in form x = f(x), then using
xn+1 = f(xn)
We want solutions to x2 = 3.
This gives xn+1 = 3/xn
This method fails to converge,
because it will oscillate between two
values regardless ?
of the starting value.
Using Newton’s method
We want solutions to
x2 – 3 = 0
f(x) = x2 – 3
f’(x) = 2x
Using xn+1 = xn – (x
?n2 – 3)/2xn
x0 = 1
x1 = 2
x2 = 7/4 = 1.75
x3 = 97/56 = 1.73214
x4 = 1.73205
x5 = 1.73205
...
Really Nice Application #2
Approximate .
Identify a function which has  as a root (Hint: think trig functions!)
Note that cos() = -1, so a root of f(x) = 1 + cos(x) is .
?
We could have also used f(x) = tan(x), provided we started between /2 and 3/2
Using Newton’s method (letting x0 = 3)
xn+1 = xn + (1 + cos(xn))/sin(xn)
x0 = 3 (a sensible starting place!)
x1 = 3.070914844
x6 = 3.139385197
?
x2 = 3.106268467
x7 = 3.140488926
x3 = 3.123932397
x8 = 3.141040790
x4 = 3.132762755
x9 = 3.141316722
x5 = 3.137177733
x10 = 3.141454688
x11 = 3.141523671
x12 = 3.141558162
x13 = 3.141575408
x14 = 3.141584031
x15 = 3.141588342
Analysis of Newton-Raphson Method
(Note that none of this bit will be in an exam)
Failure Analysis:
Approximations may diverge (and hence fail!).
Consider what happens in the following scenario on the right:
x0
Rate of Convergence:
The Newton-Raphson’s rate of convergence is ‘quadratic’: that is, as we converge
on the root, on each iteration the difference between the approximation and the
root is squared (squaring a value less than 1 makes it smaller).
That’s rather good (and is considerably better than Interval Bisection’s linear
convergence).

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