# Exercises on the bisection method/Solution

**Solution of the exercises on the bisection method**

Numerical analysis > Exercises on the bisection method/Solution

## Exercise 1 edit

- The following is a possible implementation of the bisection method with Octave/MATLAB:

```
function [x e iter]=bisection(f,a,b,err,itermax)
%The function bisection find the zeros of function
%with the bisection algorithm.
%It returns the zero x, the error e, and the number of iteration needed iter
%
%HOW TO USE IT:
%Example
%>>f=@(x)x.^3;
%>>a=-1; b=2;
%>>err=1e-5; itermax=1000;
%>>[x e iter]=bisection(f,a,b,err,itermax);
e=b-a;
iter=0;
fa=f(a);
if( fa .* f(b) >= 0 )
x =[];
disp("f(a) * f(b) >= 0! No solution!")
else
while( e > err )
iter = iter + 1;
x = 0.5 * ( b + a )
e = abs(b - x);
fx = f(x);
if( fx == 0 )
break;
elseif( fx * fa > 0 )
a = x;
fa = fx;
else
b = x;
end
if( iter == itermax)
break;
end
end
end
```

- The solution of the points 1, 2 e 3 can be found in the example of the bisection method.

For point 4 we have

- ,

so we would need at least 70 iterations. The chance of convergence with such a small precision depends on the calculatord: in particular, with Octave, the machine precision is roughly . For this reason it does not make sense to choose a smaller precision. The number of iterations, if we don't specify a maximum number, would be infinite.

## Exercise 2 edit

- To verify the existence of a root we need to show that the hypothesis roots theorem are satisfied. The first hypothesis requires to be continuous. Obviosly this is a continuous function since it is sum of two continuous functions. The second hypotheses requires the function to have oppiste signs at the interval extrema, and in fact we find
- .
- To show the uniqueness of the root we need to prove that the function is monotone and in fact
- .

- The number of iterations need is given by
- ,
- and so we have .

- The interval does not contain aany root as the second hypotesis of the roots theorem fails, in fact
- .

- In the plot we show in red the average errorand in blu the actual error. From the graph, it is clear that the actual error is not a monotone function. Moreover, note that the global behavior of both curves is the same, clarifying the term average error for .

## Exercise 3 edit

For the solution look at the convergence analysis in the bisection method page.