2. Md. Rifat Rahamatulla 09.02.05.032
Md. Reduanul Islam 09.02.05.033
Md. Imran Hossain 09.02.05.035
Md. Hasibul Haque 09.02.05.036
Md. Tousif Zaman 09.02.05.038
Presented By
3. What is non-linear equation..??
• An equation in which one or more terms have a
variable of degree 2 or higher is called a nonlinear
equation. A nonlinear system of equations contains
at least one nonlinear equation.
• Non linear equation can be solved in these ways-
• 1. Bisection method.
• 2. False position method.
• 3. Newton Raphson method.
• 4. Secant method.
5. Topic Outlines
• Definition.
• Basis of Bisection method.
• Steps of finding root.
• Algorithm of Bisection method.
• Example.
• Application.
• Advantage.
• Drawbacks.
• Improved method to Bisection method.
• Conclusion.
6. Definition
• The Bisection method in mathematics is a root
finding method which repeatedly bisects an
interval and then selects a subinterval in
which a root must lie for further processing.
7. Basis of Bisection Method
• Theorm 1-
An equation f(x)=0, where f(x) is a real
continuous function, has at least one root
between xl and xu if f(xl) f(xu) < 0.
x
f(x)
xu
x
8. Theorem 2- If the function f(x) in f(x)=0 changes sign between
two points, more than one root may exist between
the two points.
x
f(x)
xu
x
9. x
f(x)
xu
x
Theorem 3- If the function f(x) in f(x)=0 does not change sign
between two points, there may not be any roots
between the two points.
x
f(x)
xu
x
10. 10
Steps of finding root:
Step-1 Choose x and xu as two guesses for the root such
that f(x) f(xu) < 0, or in other words, f(x) changes sign
between x and xu. This was demonstrated in Figure 1.
x
f(x)
xu
x
Figure 1
12. 12
Step-3 Now check the following
a) If , then the root lies between x and xm;
then x = x ; xu = xm.
b) If , then the root lies between xm and xu;
then x = xm; xu = xu.
c) If , then the root is xm. Stop the algorithm
if this is true.
0ml xfxf
0ml xfxf
0ml xfxf
13. 13
x
x
m =
xu
2
100
new
m
old
m
new
a
x
xxm
rootofestimatecurrentnew
mx
rootofestimatepreviousold
mx
Step-4 Find the new estimate of the root
Find the absolute relative approximate error
where
14. 14
Is ?
Yes
No
Go to Step 2 using new
upper and lower guesses.
Stop the algorithm
Step-5 Compare the absolute relative approximate error with the pre-specified
error tolerance .
a
s
sa
Note one should also check whether the number of iterations is more than the
maximum number of iterations allowed. If so, one needs to terminate the algorithm
and notify the user about it.
16. 16
Example
The floating ball has a specific gravity of 0.6 and has a radius
of 5.5 cm. we are asked to find the depth to which the ball is
submerged when floating in water.
Figure 6 Diagram of the floating ball
17. 17
The equation that gives the depth x to which the ball is
submerged under water is given by
a) Use the bisection method of finding roots of equations to find
the depth x to which the ball is submerged under water.
Conduct three iterations to estimate the root of the above
equation.
b) Find the absolute relative approximate error at the end of
each iteration, and the number of significant digits at least
correct at the end of each iteration.
010993.3165.0 423
xx
18. 18
From the physics of the problem, the ball would be submerged
between x = 0 and x = 2R,
where R = radius of the ball,
that is
11.00
055.020
20
x
x
Rx
Figure 6 Diagram of the floating ball
19. To aid in the understanding of how this
method works to find the root of an
equation, the graph of f(x) is shown to the
right,
where
19
423
1099331650 -
.x.xxf
Figure 7 Graph of the function f(x)
Solution-
20. 20
Let us assume
11.0
00.0
ux
x
Check if the function changes sign between x and xu .
4423
4423
10662.210993.311.0165.011.011.0
10993.310993.30165.000
fxf
fxf
u
l
Hence
010662.210993.311.00 44
ffxfxf ul
So there is at least on root between x and xu, that is between 0 and 0.11
22. 22
055.0
2
11.00
2
u
m
xx
x
010655.610993.3055.00
10655.610993.3055.0165.0055.0055.0
54
5423
ffxfxf
fxf
ml
m
Iteration 1
The estimate of the root is
Hence the root is bracketed between xm and xu, that is, between 0.055 and
0.11. So, the lower and upper limits of the new bracket are
At this point, the absolute relative approximate error cannot be
calculated as we do not have a previous approximation.
11.0,055.0 ul xx
a
24. 24
0825.0
2
11.0055.0
2
u
m
xx
x
010655.610622.1)0825.0(055.0
10622.110993.30825.0165.00825.00825.0
54
4423
ffxfxf
fxf
ml
m
Iteration 2
The estimate of the root is
Hence the root is bracketed between x and xm, that is, between 0.055 and
0.0825. So, the lower and upper limits of the new bracket are
0825.0,055.0 ul xx
26. 26
The absolute relative approximate error at the end of Iteration 2 isa
%333.33
100
0825.0
055.00825.0
100
new
m
old
m
new
m
a
x
xx
None of the significant digits are at least correct in the estimate root of xm =
0.0825 because the absolute relative approximate error is greater than 5%.
27. 27
06875.0
2
0825.0055.0
2
u
m
xx
x
010563.510655.606875.0055.0
10563.510993.306875.0165.006875.006875.0
55
5423
ffxfxf
fxf
ml
m
Iteration 3
The estimate of the root is
Hence the root is bracketed between x and xm, that is, between 0.055 and
0.06875. So, the lower and upper limits of the new bracket are
06875.0,055.0 ul xx
29. 29
The absolute relative approximate error at the end of Iteration 3 is
a
%20
100
06875.0
0825.006875.0
100
new
m
old
m
new
m
a
x
xx
Still none of the significant digits are at least correct in the estimated root of
the equation as the absolute relative approximate error is greater than 5%.
Seven more iterations were conducted and these iterations are shown in Table
1.
31. 31
Hence the number of significant digits at least correct is given by the largest
value or m for which
463.23442.0log2
23442.0log
103442.0
105.01721.0
105.0
2
2
2
m
m
m
m
m
a
2m
So
The number of significant digits at least correct in the estimated root of
0.06241 at the end of the 10th iteration is 2.
32. Application-1
• Finding the value of resistance-
Thermistors are temperature-measuring devices based on
the principle that the thermistor material exhibits a change in
electrical resistance with a change in temperature. By
measuring the resistance of the thermistor material, one can
then determine the temperature.
33. • For a 10K3A Betatherm thermistor, the
relationship between the resistance ‘R’ of
the thermistor and the temperature is given
by
where note that T is in Kelvin and R is in ohms.
3833
ln10775468.8)ln(10341077.210129241.1
1
RxRxx
T
34. • For the thermistor, error of no more than ±0.01o C is acceptable.
To find the range of the resistance that is within this acceptable
limit at 19o C, we need to solve
and
• Use the bisection method of finding roots of equations to find
the resistance R at 18.99o C. Conduct three iterations to estimate
the root of the above equation.
3833
ln10775468.8)ln(10341077.210129241.1
15.27301.19
1
RxRxx
3833
ln10775468.8)ln(10341077.210129241.1
15.27399.18
1
RxRxx
41. 41
Application-2
Profit Counting- We are working for a start-up computer assembly
company and have been asked to determine the minimum number of
computers that the shop will have to sell to make a profit.
The equation that gives the minimum number of
computers ‘x’ to be sold after considering the total costs
and the total sales is:
03500087540)f( 5.1
xxx
42. 42
Use the bisection method of finding roots of equations to
find
The minimum number of computers that need to be
sold to make a profit. Conduct three iterations to
estimate the root of the above equation.
Find the absolute relative approximate error at the
end of each iteration, and
The number of significant digits at least correct at the
end of each iteration.
44. 44
Choose the bracket:
12500100
1.539250
f
f
0 20 40 60 80 100 120
2 10
4
1 10
4
0
1 10
4
2 10
4
3 10
4
4 10
4
f(x)
xu (upper guess)
xl (lower guess)
3.5 10
4
1.74186 10
4
0
f x( )
f x( )
f x( )
1200 x x u x l
Entered function on given interval with initial upper and lower guesses
Figure 9 Checking the sign change
between the limits.
100and50 uxx
0125001.5392
10050
ffxfxf ul
There is at least one root between
and .
x
ux
45. 45
75
2
10050
mx
0106442.41.5392
106442.475
3
3
ml xfxf
f
75and50 uxx
0 20 40 60 80 100 120
2 10
4
1 10
4
0
1 10
4
2 10
4
3 10
4
4 10
4
f(x)
xu (upper guess)
xl (lower guess)
new guess
3.5 10
4
1.74186 10
4
0
f x( )
f x( )
f x( )
f x( )
1200 x x u x l x r
Iteration 1
The estimate of the root is
Figure 10 Graph of the estimate of
the root after Iteration 1.
The root is bracketed between
and .
The new lower and upper limits of
the new bracket are
x
mx
At this point, the absolute relative approximate error cannot be calculated as we do
not have a previous approximation.
46. 46
5.62
2
7550
mx
05.6250
735.765.62
ffxfxf
f
ml
0 20 40 60 80 100 120
2 10
4
1 10
4
0
1 10
4
2 10
4
3 10
4
4 10
4
f(x)
xu (upper guess)
xl (lower guess)
new guess
3.5 10
4
1.74186 10
4
0
f x( )
f x( )
f x( )
f x( )
1200 x x u x l x r
Iteration 2
The estimate of the root is
75and5.62 uxx
The root is bracketed between
and .
The new lower and upper limits of
the new bracket are
mx
ux
Figure 11 Graph of the estimate of
the root after Iteration 2.
48. The root is bracketed between
and .
The new lower and upper limits of
the new bracket are
48
0103545.2735.76
103545.275.68
75.68
2
755.62
3
3
ml
m
xfxf
f
x
0 20 40 60 80 100 120
2 10
4
1 10
4
0
1 10
4
2 10
4
3 10
4
4 10
4
f(x)
xu (upper guess)
xl (lower guess)
new guess
3.5 10
4
1.74186 10
4
0
f x( )
f x( )
f x( )
f x( )
1200 x x u x l x r
Iteration 3
The estimate of the root is
75.68and5.62 uxx
lx
mx
Figure 12 Graph of the estimate of
the root after Iteration 3.
51. Advantage of Bisection method-
• It is a very simple method to understand.
• The bisection method is always convergent.
Since the method brackets the root, the
method is guaranteed to converge.
• Since we are halving the interval in each step,
so the method converges to the true root in a
predictable way.
51
52. • Since the method discards 50% of the interval
at each step it brackets the root in much more
quickly than the incremental search method
does. For example –
*Assuming a root is somewhere in the interval
between 0 and 1, it takes 6-7 function
evaluations to estimate the root within 0.1
accuracy.
*Those same 6-7 function evaluations using
bisecting estimate the root within 0.031
accuracy.
52
53. 5353
Drawbacks
It may take many iterations.
If one of the initial guesses is close to the
root, the convergence is slower.
The method can not find complex roots of
polynomials
The bisection method only finds root when
the function crosses the x axis.
54. 54
• If a function f(x) is such that it just touches
the x-axis it will be unable to find the lower
and upper guesses.
f(x)
x
2
xxf
56. Improved method to Bisection method-
• Regula Falsi Method: Regula Falsi method (false
position) is a root-finding method based on linear
interpolation. Its convergence is linear, but it is usually faster
than bisection
• Newton Raphson method: This method works faster
than the Bisection method and also much more accurate.
• Brent Method: Brent method combines an interpolation
strategy with the bisection algorithm. On each iteration, Brent
method approximates the function using an interpolating
curve.
57. Conclusion
This bisection method is a very simple and a robust
method and it is one of the first numerical methods
developed to find root of a non-linear equation .But
at the same time it is relatively very slow method.
We can use this method for various purpose related
to non linear continuous functions. Though it is a
slow one ,but it is one of the most reliable methods
for finding the root of a non-linear equation.