Analyzing Heron’s Algorithm

Finding the value of \sqrt{a} for a>0 is equivalent to seeking a positive number x whose square yields a: x^2=a. In other words, the solution to x^2-a=0.

We can find \sqrt{4} by solving x^2-4=0. It is 2 by mere inspection. \sqrt{9} can also be obtained by solving x^2-9=0, again, by mere inspection. But ‘mere inspection’ can only go so far. For Example, what is \sqrt{15241578750190521}?

Method for finding the square root of a positive real number date back to the acient Greek and Babylonian eras. Heron’s algorithm, also known as the Babylonian method, is an algorithm named after the 1^{st} – century Greek mathematician Hero of Alexandria, It proceeds as follows:

  1. Begin with an arbitrary positive starting value x_0.
  2. Let x_{k+1} be the average of x_k and \frac{a}{x_k}
  3. Repeat step 2 until the desired accuracy is achieved.

This post offers an analysis of Heron’s algorithm. Our aim is a better understanding of the algorithm through mathematics.

Let us begin by arbitrarily choosing a number x_0>0. If x_0^2-a=0, then x_0 is \sqrt{a}, and we have guessed the exact value of the square root. Otherwise, we are in one of the following cases:

Case 1: x_0^2-a > 0 \implies x_0^2 > a \implies \sqrt{a} x_0 > a \implies \sqrt{a} > \frac{a}{x_0} \implies \frac{a}{x_0} < \sqrt{a} < x_0

Case 2: x_0^2-a <0 \implies x_0 < \sqrt{a} \implies \sqrt{a} x_0 < a \implies \sqrt{a} < \frac{a}{x_0} \implies x_0 < \sqrt{a} < \frac{a}{x_0}

Both cases indicate that \sqrt{a} lies somewhere between \frac{a}{x_0} and x_0.

Let us define e_0 as the relative error of approximating \sqrt{a} by x_0:

e_0 =\frac{x_0-\sqrt{a}}{\sqrt{a}}

The closer e_0 is to 0, the better x_0 is as an approximation of \sqrt{a}.

Since x_0>0, \sqrt{a}>0,

e_0 + 1  = \frac{x_0-\sqrt{a}}{\sqrt{a}} + 1 = \frac{x_0}{\sqrt{a}} > 0\quad\quad\quad(1)

By (1),

x_0 = \sqrt{a} (e_0+1)\quad\quad\quad(2)

Let x_1 be the mid-point of x_0 and \frac{a}{x_0}:

x_1 = \frac{1}{2} (x_0 + \frac{a}{x_0})\quad\quad\quad(3)

and, e_1 the relative error of x_1 approximating \sqrt{a},

e_1 = \frac{x_1-\sqrt{a}}{\sqrt{a}}\quad\quad\quad(4)

We have

x_1-\sqrt{a}\overset{(3)}{=}\frac{1}{2}(x_0+\frac{a}{x_0}) - \sqrt{a} \overset{(2)}{=}\frac{1}{2}(\sqrt{a}(e_0+1)+\frac{a}{\sqrt{a}(e_0+1)})-\sqrt{a}=\frac{\sqrt{a}}{2}\frac{e_0^2}{e_0+1}\overset{(1)}{>}0\quad(5)

e_1 = \frac{x_1-\sqrt{a}}{\sqrt{a}} \overset{(5)}{=} \frac{\frac{\sqrt{a}}{2}\frac{e_0^2}{e_0+1}}{\sqrt{a}} =  \frac{1}{2}\frac{e_0^2}{e_0+1}\overset{(1)}{ > }0\quad\quad\quad(6)

By (5),

x_1 > \sqrt{a} \implies \sqrt{a} x_1 > a \implies \sqrt{a} > \frac{a}{x_1}\implies \frac{\sqrt{a}}{x_1} < \sqrt{a} < x_1

i.e., \sqrt{a} lies between \frac{a}{x_1} and x_1.

We can generate more values in stages by

x_{k+1} = \frac{1}{2}(x_k + \frac{a}{x_k}), \;k=1, 2, 3, ...\quad\quad(8)


\forall k, x_k >0.


e_k = \frac{x_k-\sqrt{a}}{\sqrt{a}}.

We have

x_k = \sqrt{a}(e_k+1)\quad\quad\quad(9)


e_{k+1} = \frac{x_{k+1}-\sqrt{a}}{\sqrt{a}}=\frac{\frac{1}{2}(\sqrt{a}(e_k+1) + \frac{a}{\sqrt{a}(e_k+1)})-\sqrt{a}}{\sqrt{a}}=\frac{1}{2}\frac{e_k^2}{e_k + 1}\quad\quad\quad(10)

Consequently, we can prove that

\forall k \ge 1, e_{k+1} > 0\quad\quad\quad(11)

by induction:

When k = 1, e_{1+1} \overset{(10)}{=} \frac{1}{2}\frac{e_1^2}{e_1+1} \overset{(6)} {>} 0.

Assume when k = p,

e_{p+1} =\frac{1}{2}\frac{e_p^2}{e_p^2+1} >0\quad\quad\quad(12)

When k = p + 1, e_{(p+1)+1} \overset{(10)}{=}\frac{1}{2}\frac{e_{p+1}^2}{e_{p+1} + 1}\overset{(12)}{>}0.

Since (11) implies

\forall k \ge 2, e_k > 0\quad\quad\quad(13)

(10) and (13) together implies

\forall k \ge 1, e_k > 0\quad\quad\quad(14)

It follows that

\forall k \ge 1,  0 \overset{(14)}{<} e_{k+1} \overset{(10)}{=} \frac{1}{2}\frac{e_k^2}{e_k+1} \overset{(14)}{<} \frac{1}{2}\frac{e_k^2}{e_k}=\frac{1}{2}e_k\quad\quad\quad(15)


\forall k \ge 1, 0 \overset{(14)}{<} e_{k+1} \overset{(10)}{=} \frac{1}{2}\frac{e_k^2}{e_k+1}\overset{(14)}{<}\frac{1}{2}\frac{e_k^2}{1}<e_k^2\quad\quad\quad(16)

(15) indicates that the error is cut at least in half at each stage after the first.

Therefore, we will reach a stage n that

0 < e_n < 1\quad\quad\quad(17)

It can be shown that

\forall k \ge 1, 0< e_{n+k} < e_n^{2^k}\quad\quad\quad(18)

by induction:

When k = 1,

0 \overset{(14)}{<} e_{n+1} \overset{(10)}{=}\frac{1}{2}\frac{e_n^2}{e_n+1} \overset{}{<} e_n^2=e_n^{2^1}\quad\quad\quad(19)

Assume when k = p,

0 < e_{n+p} < e_n^{2^p}\quad\quad\quad(20)

When k = p+1,

0 \overset{(14)}{<} e_{n+(p+1)} = e_{(n+p)+1} \overset{(16)}{<}e_{n+p}^2\overset{(20)}{<}(e_n^{2^p})^2 = e_n^{2^p\cdot 2} = e_n^{2^{p+1}}

From (17) and (18), we see that eventually, the error decreases at an exponential rate.

Illustrated below is a script that implements the Heron’s Algorithm:

a   - the positive number whose square root we are seeking
x0  - the initial guess
eps - the desired accuracy
square_root(a, x0, eps) := block(
        if abs(xk^2-a) < eps then return(xk),

Suppose we want to find \sqrt{2} and start with x_0 = 99. Fig. 2 shows the Heron’s algorithm in action.

Fig. 1

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