Bucking down to the Bakhshali manuscript

The Bakhshali manuscript is an artifact discovered in 1881, near the town of Peshawar (in then British India, but now in present-day Pakistan). It is beautifully described in an article in the online magazine of the American Mathematical Society and I spent a few hours fascinated by the description in the article (written excellently by Bill Casselman of the University of British Columbia). It is not clear how old the 70 page manuscript fragment is – its pieces date (using radiocarbon dating) to between 300-1200 AD. However, I can’t imagine this is simply only as old as that – the mathematical tradition it appears to represent, simply by its evident maturity, is much older.

Anyway, read the article to get a full picture, but I want to focus on generalizing the approximation technique described in the article. One of the brilliant sentences in the article referred to above is “Historians of science often seem to write about their subject as if scientific progress were a necessary sociological development. But as far as I can see it is largely driven by enthusiasm, and characterized largely by randomness”. My essay below is in this spirit!

The technique is as follows (and it is described in detail in the article)

Suppose you want to find the square root of an integer $N$ , which is not a perfect square. Let’s say you find an integer $p_1$ whose square is close to $N$ – it doesn’t even have to be the exact one “sandwiching” the number $N$ . Then define an error term $\mathcal E_1$ in the following way,

$N = p_1^2 + \mathcal E_1$

Next, in the manuscript, you are supposed to define, successively

$p_2 = p_1 + \frac{\mathcal E_1}{2 p_1}$

and compute the $2^{nd}$ level error term using simple algebra

$\mathcal E_2 = - \frac{\mathcal E_1^2}{4 p_1^2}$

This can go on, according to the algorithm detailed in the manuscript and write

$p_3 = p_2 + \frac{\mathcal E_2}{2 p_2}$

and compute the error term with the same error formula above, with $p_2$ replacing $p_1$ . The series converges extremely fast (the $p_n$ approaches the true answer quickly).

Here comes the generalization (this is not in the Bakhshali manuscript, I don’t know if it is a known technique) – you can use this technique to find higher roots.

Say you want to find the cube root of $N$ . Start with a number $p_1$ whose cube is close to $N$ . Then we define the error term $\mathcal E_1$ using

$N = p_1^3 + \mathcal E_1$

Next, define $p_2 = p_1 + \frac{\mathcal E_1}{3 p_1^2}$

and compute the $2^{nd}$ level error term using simple algebra

$\mathcal E_2 = -3 \frac{\mathcal E_1^2}{9 p_1^3} - \frac{\mathcal E_1^3}{27 p_6}$

and carry on to define $p_3 = p_2 + \frac{\mathcal E_2}{3 p_2^2}$ and so on.

The series can be computed in Excel – it converges extremely fast. I computed the cube root of $897373742731$ to Excel’s numerical accuracy in six iterations.

Carrying on, we can compute the fourth root of $N$ , as should be obvious now, by finding a potential fourth root $p_1$ that is a close candidate and then

$p_2 = p_1 + \frac{\mathcal E_1}{4 p_1^3}$

and so on.

You can generalize this to a general $q^{th}$ root by choosing $p_2=p_1 + \frac{\mathcal E_1}{q p_1^{q-1}}$ and carrying on as before. It is a little complex to write down the error term, but it just needs you to brush up the binomial theorem expansion a little. I used this to compute the $19^{th}$ root of $198456$ , starting from the convenient $2$ , rather than $1$ (see below!). It is $1.900309911$ . If I started from the intial number $3$ , convergence is slower but it still gets there quite fast (in twelve iterations).

Here’s a bit of the Excel calculation with a starting point of 2

	p	E
p_1	2	-325832
p_2	1.93458156	-80254.8113
p_3	1.90526245	-10060.9486
p_4	1.90042408	-226.670119
p_5	1.90030997	-0.12245308
p_6	1.90030991	-3.6118E-08
p_7	1.90030991	0

and here’s a starting point of 3. Notice how much larger the initial error is (under the column E).

	p	E
p_1	3	-1162063011
p_2	2.84213222	-415943298
p_3	2.69261765	-148847120
p_4	2.55108963	-53231970.3
p_5	2.41732047	-19003715.9
p_6	2.29140798	-6750923.06
p_7	2.17425159	-2365355.74
p_8	2.06867527	-797302.733
p_9	1.98149708	-240959.414
p_10	1.92430861	-53439.1151
p_11	1.90282236	-5045.06319
p_12	1.90033955	-58.8097322
p_13	1.90030991	-0.00825194
p_14	1.90030991	0 : CONVERGED!

The neat thing about this method is, unlike what Bill Casselman states, you don’t need to start from an initial point that sandwiches the number $N$ . If you start reasonably close (though never at 1, for obvious reasons!), you do pretty well. The reason why these iterations converge is that the error term $\mathcal E_m$ is always smaller than $p_q^{m-1}$ .

The actual writer of the Bakhshali manuscript did not use decimal notation, but used rational numbers (fractions) to represent decimals, which required an order of magnitude more work to get the arithmetic right! It is also interesting how the numerals used in the manuscript are so close to the numerals I grew up using in Hindi-speaking Mumbai!

Note added: Bill Casselman wrote to me that the use of rational numbers with a large number of digits instead of decimals represents “several orders of magnitude” more difficulty. I have no difficulty in agreeing with that sentiment – if you want the full analysis, read the article.

There is also a scholarly analysis by M N Channabasappa (1974) that predates the AMS article that analyzes the manuscript very similarly.

A wonderful compendium of original articles on math history is to be found at Math10.

4 Comments

R Gopu says:

June 7, 2018 at 9:53 pm

Excellent demonstration of the iteration and the algorithm.

“Historians of science often seem to write about their subject as if scientific progress were a necessary sociological development. But as far as I can see it is largely driven by enthusiasm, and characterized largely by randomness”

Geometry was like chess to Greeks, a recreational activity for the intellect. Contemporary Indians seem to have indulged in grammar and etymology with the same fervor.

William Goldman observes in his book “Adventures in the Screentrade” that cinema started as a fad, before taking off as an industry. Same goes for computers, Facebook, even science in general.

1. Simply Curious says:
  
  June 10, 2018 at 11:42 am
  
  I much prefer the description of science as a voyage of discovery, rather than an academic discipline, which brings to mind, as one Soviet writer of many years ago wrote – “white coated experimenters pacing around in lab coats, pricking and prodding a poor laboratory rabbit, testing out some inconsequential result and then throwing out the carcass after having finished an utterly useless and pointless experiment, but having produced a paper”
  
kameshaiyer says:

February 20, 2022 at 1:11 am

Your proposal is a generalization of Newton-raphson (among others):
Delta(y) = (dy/dx)*Delta(x)
becomes (for y=x^n)
Delta(x) = Delta(y)/n*x^(n-1)
i.e. x1 – x0 = (y-x0^n)/n*x0^(n-1)
The series x0,x1,x2,…will mosrly converge – there are the usual small set of extreme cases h that will not converge- that’s another story.

1. Simply Curious says:
  
  February 20, 2022 at 9:12 am
  
  Very insightful indeed. In Latex format, for easy reading, the above derivation is
  $\delta_y = \frac{dy}{dx} \times \delta_x$
  becomes $\: (for \: y \: = \: x^n)$
  $\delta_x =\frac{\delta_y}{n \times x^{n-1}}$
  i.e.,
  $x_1 - x_0 = \frac{y-x_0^n}{n \times x_0^{n-1}}$
  and so on.
  Thanks for the note!