On the computation of the n'th decimal digit
of various transcendental numbers.

by Simon Plouffe

November 30, 1996
Revised in March 2003


We outline a method for computing the n'th decimal (or any other base)
digit of Pi in C*n^3*log(n)^3 time and with very little memory. The
computation is based on the recently discovered Bailey-Borwein-Plouffe
algorithm and the use of a new algorithm that simply splits an ordinary
fraction into its components. The algorithm can be used to compute other
numbers like Zeta(3), Pi*sqrt(3), Pi^2 and 2/sqrt(5)*log(tau) where tau
is the golden ratio. The computation can be achieved without having to
compute the preceeding digits. We claim that the algorithm has a more
theoretical rather than practical interest, we have not found a faster
algorithm, nor have we proven that one does not exist.

The formula for Pi used is

----- n 2
\ n 2 (n!)
) ---------- = Pi + 3 (1)
/ (2 n)!
n = 1

* Introduction
* Key observation and the Splitting Algorithm
* Other numbers
* Conclusion
* Bibliography


The computation of the n'th digit of irrational or transcendental
numbers was considered either impossible or as difficult to compute
as the number itself. Last year (1995) [BBP], have found a new way
of computing the n'th binary digit of various constants like Pi
and log(2).
An intensive computer search was then carried out to find if
that algorithm could be used to compute a number in an arbitrary
base. We present here a way of computing the n'th decimal digit
of Pi (or any other base) by using more time than the [BBP]
algorithm but still with very little memory.

Key observation and formula

The observation is that a fraction 1/(a*b) can be split into
k1/a + k2/b by using the continued fraction algorithm of a/b.
Here a and b are two prime powers. This is equivalent to having
to solve a diophantine equation for k1 and k2 - it is always
possible to do so if (a,b) = 1 , see [HW] if they have no common
factor. If we have more than 2 prime factors then it can be
done by doing 2 at the time and then using the result to combine
with the third element. This way an arbitrary big integer M
can be split into small elements. If we impose the conditions
on M of having only small factors (meaning that the biggest
prime power size is of the order of a computer word), then
an arbitrary M can be represented. If this is true then a number
of known series and numbers
can then be evaluated. For example the expression 1/C(2*n,n),
the central binomials satisfies that : the prime powers of
this number are small when n is big.


1/C(100,50) = 1/100891344545564193334812497256 =

3 4
2 * 3 *11*13*17*19*29*31*53*59*61*67*71*73*79*83*89*97

Now if we take 2 elements at the time and solve the simple
diophantine equation and proceed this way

1) 1/(a*b) = k1/a + k2/b
2) (k1/a + k2/b)/c = m1/a + m2/b + m3/c
3) we proceed with the next element.

At each step the constants k1 and k2 are determined by simply
expanding a/b into a continued fraction and keeping the 'before
last' continuant, later m1, m2 and m3 are determined the same
way. Having finished with that number we quickly arrive at a
number which is (modulo 1) the same number but represented as
a sum of only small fractions.

So, 1/100891344545564193334812497256 =

-3/8 - 61/81 - 1/11 - 11/13 - 4/17 - 9/19 - 25/29 - 26/31 +
23/53 + 41/59 + 29/61 + 37/67 + 33/71 + 19/73 + 36/79 + 7/83
+ 13/89 + 88/97

The time taken to compute this expression is log(n)*n^2, log(n)
being the time spent to compute with the euclidian algorithm
on each number. We did not take into account the time spent on
finding what is the next prime in the expression simply because
we can consider (at least for the
moment) that the applicability of the algorithm is a few thousands
digits and so the time to compute a prime is really a matter
of a few seconds in that range for the whole process. Since we
know by advance what is the maximal prime there could be in
C(2*n,n) then we can do it with a greedy algorithm that pulls
out the factors until we reach 2*n,
and this can be done without having to compute the actual number
which would obviously not fit into a small space. It can be
part of the loop without having to store any number apart from
the current n. For any p in binomial(2*n,n) the maximal
exponent is (as Robert Israel pointed out).

2 n
\ / 2 n n \
) | [---] - [---]|
/ | k k |
----- \ p p /
k = 1

Equivalently, for p = 2 it gives the number of '1' in the binary
expansion of n, for p = 3 there is another clue with the ternary
expansion of the number and the number of times the pattern '12'
appears. Now looking at the sum(1/C(2*n,n),n=1..infinity), we
can say that the series is essentially Pi*sqrt(3) since it differs
only by 4/9*Pi*sqrt(3) + 1/3, since these are 2 small rationals
we can use BBP algorithm to carry the computation to an arbitrary
position in almost no time. Having n/C(2*n,n) instead of (1) only
simplifies the process.

To compute the final result of each term we need only few memory

* 1 for the partial sum so far. (evaluated later
with the BBP algorithm).
* 4 for the current fractions k1/a and k2/b.
* 2 for the next element to be evaluated : 1/c.
* 1 for n itself.

So with as little as 8 memory elements the sum for each term
of (1) can be carried out a without having to store any number
greater than a computer word in log(n) time, adding this for
each element the total cost for (1) is then n^3*log(n).

The next thing we have to consider is that , if we have an
arbitrary large M and if M has only small factors then 1/M
can be computed. First, we need to represent 1/M as
Sum(a(i)/p(i)**(j),i=1..k) where p(i)^(j) is a prime
power and a(i) is smaller than p(i)^(j).

If we have 2^n/M then by using the binary method on each
element of the representation of 1/M with (2) is possible
in log(n) time. Again if we don't want to store the element
of (2) in memory we can do it as we do the computation of
the first part at each step. In this algorithm we can either
store the powers of 2 to do the binary method or not. There
is variety of ways to do it, we refer to [Knuth vol. 2] for

This step is important, essentially once we can represent
1/(a*b) by splitting them then to multiply by 2^n only adds
log(n) steps for each element and it can be done in arbitrary
base since we have the actual fraction for each element of
(2). It only pushes the decimal (or the the decimal point of
the base chosen), further. At any moment only one element in
the expansion of 1/M is considered with the current fraction,
that same fraction can be represented in base 10 at anytime
if we want the decimal expansion at that point. For this reason
multiplying the current fraction by 2^n involves only small
numbers and fractions.

Once this is done, the total cost becomes n^3*log(n)^2. This
cost is for the computation of the k'th partial sum of

----- n
\ 2
) ----------- (2)
/ n C(2 n, n)
n = 1

where C(2*n,n) is the central binomial coefficient. If we want
at each step to compute (the final n'th digit) then we need
log(n) steps to do it. It can be done in any base chosen in
advance, in BBP the computation could be done in base 2 but
here we have the actual explicit fraction which is independant
of the base. This is where we actually compute the decimal
expansion of the final fraction of the process.
So finally the n'th digit of Pi can be computed in
C*n**3*log(n)**3 steps.

Other Numbers

By looking at the plethora of formulas of the same type as
(1) or (3) we see that [PIagm], [RamI and IV] the numbers
Pi*sqrt(3) Pi, Pi**3, Zeta(3) and even powers of Pi can be
computed as well. The condition we need to enseure is :
if any term of a series can be split into small fractions of
size no greater than that of a computer word, then it is part
of that class. This includes series of the type :

----- n
\ c
) --------------- (3)
/ r
----- P(n) C(m n, n)
n = 1

where c is an integer, P(n) a polynomial and C(mn,n) is a near
central binomial coefficient. This class of series contains
many numbers that are not yet identified in terms of known
constants and conversely the known constants that are of similar
nature like Zeta(5) have not yet been identified as members
of the class. The process of identifying a series as being
expressed in terms of known constants and the exact reverse
process is what the Inverter[Inv] tries to do.

The number e or exp(1) which is sum(1/n!,n=0..infinity) does
not satisfies our condition because 1/n! eventually contains
high powers of 2, therefore cannot be computed to the n'th digit
using our algorithm.
The factorisation of 1/n! has high powers of small primes,
the highestis 2**k and k is nearly the size of n. For this
particular number only avery few series are known and appear
to be only a variation on that first one.

Others like gamma or Catalan do not seem to have a proper series
representation and computer search using Bailey's PSLQ or LLL with
MapleV and Pari-Gp gave no answer to this. Algebraic numbers like
sqrt(2) have not been yet been fully investigated and we still do
not know if those would fall into this category.


There are many, but first and foremost we cannot resist thinking at
William Shanks who did the computation of Pi by hand in 1853 - if he
would had known this algorithm, he would have certainly tried it
before spending 20 years of his life computing Pi (half of it on a mistake).
Secondly, the algorithm shown here is theoretical and not practical.
We do not know if there is a way to improve it, and if so then it is
reasonable to think that it could then be used to check long computations
like the one that Yasumasa Kanada conducted last year for the computation
of Pi to 1241 billion digits. There could be a way to
speed the algorithm to make it an efficient algorithm.
Thirdly, so far there are 2 classes of numbers that can be computed to the
n'th digit :
1) the SC(2) class as in the [BBP] algorithm which includes various
2) this new class of numbers. Now what's next ?, so far we do not know
wheter, for example, series whose general term is H(n)/2**n (where H(n)
is the n'th harmonic number) whcich fall into the first class, can be
extended. We think that this new approach is only the tip of the iceberg.
Finally, it is interesting to observe that we can then compute Pi to the
10000'th digit without having to store (hardly) any array or matrix, so it
can be computed using a small pocket calculator. We also note that, in some
way we have a way to produce the digits of Pi without using memory, this means
that the number is compressible , if we consider that we could use the
algorithm to produce a few thousands digits of the number. We think that
other numbers are yet to come and that there is a possibility (?) of having
a direct formula for the n'th digit (in any base) of a naturally occuring
constant like log(2).


We wish to thanks, Robert Israel (Univ. British Columbia) and David H.
Bailey [NASA] for their helpful comments.


* David H. Bailey, Peter B. Borwein and Simon Plouffe, On The Rapid Computation
of Various Polylogarithmic Constants, april 1997 in Mathematics of Computation.
* Bruce C. Berndt, Ramanujan Notebooks vols. I to IV. Springer Verlag, New York.
* Pi and the AGM, by Jonathan M. Borwein and Peter B. Borwein, A Study in
Analytic Number Theory and Computational Complexity, Wiley, N.Y., 1987.
* David H. Bailey and Simon Plouffe, Recognizing Numerical Constants, preprint 1995.
* Robert Israel at University of British Columbia, personal communication.
* M. Abramowitz and I. Stegun, Handbook of Mathematical Functions, Dover, New York, 1964.
* G. H. Hardy and E. M. Wright, An Introduction to the Theory of Numbers 5e,
Oxford University Press, 1979.
* W. Shanks, Contributions to Mathematics Comprising Chiefly of the Rectification
of the Circle to 607 Places of Decimals, G. Bell, London, 1853.
* D.E. Knuth, The Art of Computer Programming, Vol. 2: Seminumerical Algorithms,
Addison-Wesley, Reading, MA, 1981.
* Plouffe's Inverter at http://pi.lacim.uqam.ca/eng

Keywords : Pi, complexity, algorithm, n'th digit computation, Plouffe's Inverter.