1. Introduction
In this note, we study Stirling’s approximation to
, a fundamental result in asymptotic analysis and number theory. Originally derived in the 18th century, the approximation provides an accurate estimate of the factorial function for large values of
N. Over the centuries, various forms and refinements of this approximation have been developed. A particularly elegant and instructive derivation, based solely on elementary transformations of integrals and term-by-term integration of a series, was introduced by Marsaglia [
1].
Our first objective is to briefly derive Stirling’s formula. We begin with the representation of
as a sum of logarithms:
where each term
measures the discrepancy between the trapezoidal rule and the exact integral of
over the interval
. That is,
The integral in (
1) can be evaluated explicitly,
is a telescoping sum, as the terms cancel pairwise. In this way,
which leads to
where
is given in (
2).
It is well known, from the Mercator series, that
Substituting
, where
, yields
Thus,
in (
2) can be rewritten as
where
, for
, is a positive decreasing sequence of real numbers that converges to zero.
From the Riemann Zeta function (see [
2] for details),
which is convergent for
with
, it follows that
and, therefore,
From [
2,
3],
where
is the Euler-Mascheroni constant.
Additionally, from [
3,
4], based on the Riemann Zeta function and Rational Zeta series, it follows that
Moreover, using the Mercator series evaluated at
,
we arrive at the Suryanarayana formula (see [
3] for example),
and, consequently,
Therefore,
where
is given by
and, as before,
, for
, is a positive decreasing sequence of real numbers that converges to zero.
Finally, Eq. (
3), known in the literature as Stirling’s formula, can be expressed as
where the remainder
is given in (
4).
Eq (
5) was first presented in [
5], where it was also established that
. Since then, the term
has been the subject of detailed study (see [
6,
7,
8,
9] for historical notes), particularly through works such as [
10], who first provided sharp two-sided bounds for it,
Several years later, [
11] improved the estimation of
,
More recently, [
12] obtained the following result,
[
8] proved that
with the best possible constants
and
, and [
9] derived the following inequality:
It is also important to highlight the works [
1,
7,
13,
14,
15] who focused on obtaining explicit formulas for the coefficients of the asymptotic expansion of
.
The main goal of this work is to present an algorithm, relying only on elementary mathematical methods techniques, for efficiently calculating many terms of exactly. To the best of our knowledge, we understand that our algorithm represents an alternative to the procedures presented herein.
2. The Algorithm
Observe that
in (
4) can be rewritten as
a numerical series whose general term forms a convergent alternating series for each
N. Define
the sequence of partial sums of this series.
Step 1: Consider the polynomial
defined by the following equation:
where
is a real parameter to be determined later. This leads to:
Now,
is chosen in order to reduce the degree of
to the lowest possible value. In this case,
and, after some calculations, we obtain
which is a polynomial of degree 1, that is,
. We denote by
the coefficient associated with the highest degree term of the polynomial
.
In this way, the partial sum
can be expressed as
As an important remark,
in (
4) can then be approximated by
The sum above can be estimated using the integral test, comparing it to an improper integral.
Step 2: Similarly, we seek a polynomial
that satisfies
where
is a real parameter to be determined.
Again, is chosen with the aim of reducing the degree of to the lowest possible value. In this case, .
Therefore, we can present
as
where
Note that
and
.
Again, as a remark,
in (
4) is better approximated by
Step 3: Similarly, aiming for a better approximation of
, we write
where
is, as before, a real parameter to be determined.
The polynomial
can be rewritten as
The parameter is chosen with the aim of reducing the degree of to the lowest possible value. Thus, .
Then,
is given by
where
Note that
and
.
Therefore, a better approximation for
in (
4) is obtained:
Step 4: Likewise, the polynomial
is defined as
Taking
, we obtain
where
and
.
Thus,
and then
where
.
In summary, the following algorithm is presented.
Remark 1.
The sequence , for , defined above, is crucial because it leads to
Furthermore, based on computational tests, it was observed that, for ,
The exact calculations of
and
, for
, were carried out using symbolic computations in the Mathematica software [
16], using the script that follows:

To illustrate our algorithm, we calculate
using the Mathematica script provided above (CPU time approximately
seconds, executed in the trial version of Mathematica Online) and then,
in (
4) can be approximated by
As a test, the CPU time to calculate the first 300 terms of was approximately 200 seconds. Remark 1 have been verified in this case.
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