Minimum number of iterations in Newton's method to find a square root

I am writing an algorithm that evaluates the square root of a positive real number $y$. To do this I am using the Newton-Raphton method to approximate the roots to $f(x)=x^2-y$. The $n^{th}$ iteration gives $$x_n=\frac{x_{n-1}^2+y}{2x_{n-1}}$$ as an approximation to $\sqrt{y}$. I found that starting with an initial guess $x_0=1$ works pretty well generally, so an answer to the question below that assumes $x_0=1$ is fine.

My question: is there an exact expression for the minimum $N$ of iterations needed to attain a given precision $p$ in the approximate solution $x_N$? In other words I'm looking for the smallest integer $N$ such that $$\left|\frac{x_N-\sqrt y}{\sqrt y}\right|<p.$$

I've thought about this for a while and played around with the expression for the errors $\epsilon_n = x_n - \sqrt y$ which can be shown to satisfy $\epsilon_{n+1}=\epsilon_n^2\,/\,2x_n$, but I can't find an answer. I've looked around on Google but I couldn't find an answer either.

Any pointers to a solution online or help would be much appreciated. A follow-up would of course be: can $x_0$ be optimised (while being a simple enough expression in terms of $y$) in order to minimise $N$?

• I don't have an answer to your overall question, but here's a little bit of intuition: $x_n$ is the average of $x_{n-1}$ and $y/x_{n-1}$. One of these numbers must be less than $\sqrt{y}$, and one must be greater, so the average is a reasonable update. And of course, the closer $x_0$ is to $\sqrt{y}$, the better. Something like $x_0 = \lfloor \sqrt{y}\rfloor$ is a good guess (take the biggest perfect square less than $y$, and start with its square root). – BaronVT Nov 9 '13 at 16:58
• Try analyzing $x_n^2 - y$ rather than $x_n - \sqrt{y}$. I imagine you probably want to analyze the roundoff error too, rather than assuming addition, multiplication and division are exact, which makes the problem more complicated. – Hurkyl May 2 '14 at 8:21

There is such a formula: consider

$$\frac{x_n+\sqrt y}{x_n-\sqrt y}=\frac{\frac{x_{n-1}^2+y}{2x_{n-1}}+\sqrt y}{\frac{x_{n-1}^2+y}{2x_{n-1}}-\sqrt y}=\frac{(x_{n-1}+\sqrt y)^2}{(x_{n-1}-\sqrt y)^2}=\left(\frac{x_{n-1}+\sqrt y}{x_{n-1}-\sqrt y}\right)^2.$$

By recurrence,

$$\frac{x_n+\sqrt y}{x_n-\sqrt y}=\left(\frac{x_{0}+\sqrt y}{x_{0}-\sqrt y}\right)^{2^n}.$$

If you want to achieve $2^{-b}$ relative accuracy, $x_n=(1+2^{-b})\sqrt y$,

$$2^n=\frac{\log_2\frac{(1+2^{-b})\sqrt y+\sqrt y}{(1+2^{-b})\sqrt y-\sqrt y}}{\log_2\left|\frac{x_{0}+\sqrt y}{x_{0}-\sqrt y}\right|},$$

$$n=\log_2\left(\log_2\frac{2+2^{-b}}{2^{-b}}\right)-\log_2\left(\log_2\left|\frac{x_{0}+\sqrt y}{x_{0}-\sqrt y}\right|\right).$$

The first term relates to the desired accuracy. The second is a penalty you pay for providing an inaccurate initial estimate.

If the floating-point representation of $y$ is available, a very good starting approximation is obtained by setting the mantissa to $1$ and halving the exponent (with rounding). This results in an estimate which is at worse a factor $\sqrt 2$ away from the true square root.

$$n=\log_2\left(\log_2\left(2^{b+1}+1\right)-\log_2\left(\log_2\frac{\sqrt 2+1}{\sqrt 2-1}\right)\right) \approx\log_2(b+1)-1.35.$$ In the case of single precision (23 bits mantissa), 4 iterations are always enough. For double precision (52 bits), 5 iterations.

On the opposite, if $1$ is used as a start and $y$ is much larger, $\log_2\left|\frac{1+\sqrt y}{1-\sqrt y}\right|$ is close to $\frac{2}{\ln(2)\sqrt y}$ and the formula degenerates to $$n\approx\log_2(b+1)+\log_2(\sqrt y)-1.53.$$ Quadratic convergence is lost as the second term is linear in the exponent of $y$.

If your starting value of $y$ is correctly "scaled" between $1$ and $100$, then a good initial guess is

$X_0 = 1,1545 + 0,11545*y$ (linear approx) or if you prefer a second order approx. you can use:

$X_0 = 1,0 + y*( 0,18 - 0,0009*y)$

Using $X_0$, you need only 3 iterations (Newton / Heron) to get a Relative Error less than 0,0001 % !

Now, here is a fast method to get an excellent $X_0$ (with less than 1% error):

Store a pre-calculated constants table containing all the square roots with rounded 4 decimals in the range $[1 ; 100]$. So $table(i)=sqrt(i)$.

Use Simple Linear Interpolation to get a good $X_0$. Now, the Maximum Relative Error is still greater than 1% ! ( 1.5% near $y=1.4142$).

You can simply divide this Maximum Relative Error (MRE) by a factor of 2 by doing this trick:

Simply replace the 1st entry ($\sqrt(1)=1.0000$ by $\sqrt(1)=1.0075$ ) AND the second entry ( $\sqrt(2)=1.4142$ by $\sqrt(2)=1.4248$ ). That's all. You have a MRE of 0,75%! So, the WORST case is a $X_0$ with only 0.75% in the WHOLE RANGE $[1 ; 100]$.

Unscale your number, and with only ONE Newton / Heron iteration get a 0.0028% MRE in any case, and with two iteration you have a 0.00000004% MRE!!

That's Fast, Simple and very Accurate. Have fun.