**INTRO:**

The

**Bisection Method**is a very well-known root-finding algorithm that always comes at the very beginning of every book on Numerical Analysis.

The algorithm searches for a root in the interval, $latex [a_{0},b_{0}]$ in whose endpoints the

**continuous**problem function, $latex f(x)$, takes opposite signs:

$latex f(a_{0})f(b_{0})<0$ The

**Bisection Method**is based on Bolzano's Intermediate Value Theorem, and it can give, also, an alternate proof for it. (Reference [1])

Here, it does not matter function values: The algorithm does not use any other information from $latex f(x)$, other than its sign changes.

There are excelent pages on the internet that deal with this topic, (references [1], [2] and [3] for example), and it would be useless to add more redundant and, for sure, worse presented summary about this numerical method.

I just want to take out something hidden inside the computer software, a small piece of math inside the programming area.

**THE LOST FUNCTION**

The core of the

**Bisection Method**is the conditional

**IF... THEN...**sentence, where the algorithm chooses in which half of the interval is the desired root.

This conditional evaluation can be converted into a

**decision function**, Let's say $latex \delta_i$, gives $latex 0$ if the root is on the left half interval and $latex 1$, otherwise.

Now the iterative sequence can be rewritten using this function, $latex \delta_i$, where:

$latex m_{i}$ is the midpoint of the search interval:

$latex \displaystyle m_{i}=\frac{a_{i}+b_{i}}{2}$

$latex \delta_{i}$ is the decision function, and the $latex \delta(i,j)$, in the second member, is the Kronecker's delta:

$latex \delta_i=\delta(sgn(f(m_{i})),sgn(f(a_{i})))$

Then:

$latex \delta_{i} = \left\{ \begin{array}{ll} 1 & \mbox{if } (sgn(f(m_{i})=sgn(f(a_{i})) \\\\ 0 & \mbox{if } (sgn(f(m_{i})\neq sgn(f(a_{i})) \end{array}$

The endpoints of the search interval must be:

$latex {[a_{i+1}, b_{i+1}]} = \left\{ \begin{array}{ll} {[m_{i},b_{i}]} & \mbox{if }(\delta_{i}=0) \\\\ {[a_{i},m_{i}]} & \mbox{if } (\delta_{i}=1) \end{array}$

Then, they can be expressed with the aid of $latex \delta_{i}$, as:

$latex a_{i+1}=a_{i}+\delta_{i}\cdot (m_{i}-a_{i})$

$latex b_{i+1}=b_{i}+(1-\delta_{i})\cdot (m_{i}-b_{i})$

*Note:*

If $latex f(x)$, has only a single root in the interval $latex [a_{0},b_{0}]$, then it is possible to use an alternate version of the decision function:

$latex \delta_i=\delta(sgn(f(m_{i})),sgn(f(a_{0})))$

with the advantage that then it is not necessary to evaluate $latex f(a_{i})$ in the main loop of the algorithm: this implies an improvement in performance.

**LENGTHS RATIO:**

We can name lengths ratio between intervals $latex [a_{0},a_{i}]$, and $latex [a_{0},b_{0}]$, as the parameter:

$latex \theta_{i}=\frac{a_{i}-a_{0}}{b_{0}-a_{0}}$

If $latex \theta_{i}$ is expressed as a function of $latex \delta_{i}$:

$latex \displaystyle \theta_{i}=\sum_{k=0}^{i-1} \frac{\delta_{k}}{2^{k+1}}$

The sequence formed by the distinct values of the decision function, $latex \delta_{i}$, matches up with the

**binary expansion**(or the binary digits) of $latex \theta_{i}$

Then the midpoint and the endpoints of the interval in function of $latex \theta_{i}$, are:

$latex a_{i}=a_{0}+(b_{0}-a_{0})\cdot \theta_{i}$

$latex \displaystyle m_{i}=a_{0}+(b_{0}-a_{0})\cdot \left(\theta_{i}+\frac{1}{2^{i+1}}\right)$

$latex \displaystyle b_{i}=a_{0}+(b_{0}-a_{0})\cdot \left(\theta_{i}+\frac{1}{2^{i}}\right)$

**HACKING BISECTION METHOD:**

The connection between $latex \delta_{i}$ and $latex \theta_{i}$ can be used to turn Bisection Method into a method to find the binary expansion of a root of any function that holds with theorem hypothesis.

For example if we take:

$latex f(x)=x^2-x_{0}$, where $latex x_{0}>0$

with $latex a_{0}=0$ and $latex b_{0}=2^{k}$, where $latex 2^{k-1} < \sqrt{x_{0}} < 2^{k}$, then: $latex \sqrt{x_{0}}=\lim_{i \to +\infty}{\theta_{i}}$ and the sequence of $latex \delta_{i}$ gives the binary digits of $latex \sqrt{x_{0}}$

**Archives:**

[a]-010910-Binary Bisection.nb

**References:**

[1]-Mohamed A. Khamsi, Helmut Knaust - SOSMATH - The Bisection Method

[2]-John H. Mathews - California State Univ. Fullerton - Module for The Bisection Method

[3]-Holistic Numerical Methods Institute- - Bisection Method: Nonlinear Equations