# cade.site — DIY: Gamma and Zeta Function Implementation

*
theme:
*

# DIY: Gamma and Zeta Function Implementation

in developing my language, kscript, i wanted to have the Riemann Zeta Function and Gamma Function available as part of the standard library. so, i implemented it!

our basic workflow is:

- write a python script that can generate some C code that corresponds to both functions
- run that python script with enough precision for
`double`

in C, with correct error bounds

let’s get to it!

## Definitions

i’ll assume you’re more or less familiar with what the Zeta and Gamma functions are, but i’ll also provide a definition we can work with:

\[\Gamma(x) = (x - 1)! = \int_{0}^{\infty} t^{x-1} e^{-t} dt\] \[\zeta(x) = \sum_{n=1}^{\infty} \frac{1}{n^x}\]we use the following reflection formulas to define the value elsewhere:

\[\zeta(x) = 2 (2 \pi) ^ {x - 1} \sin(\frac{x \pi}{2}) \Gamma(1 - x) \zeta(1 - x)\] \[\Gamma(x) = \sin(\pi x) \Gamma(1 - x)\]## Goal

our goal is to define `C`

functions with the following signatures, which evaluate the specific function at a particular point:

```
double my_zeta(double x);
double complex my_czeta(double complex x);
double my_gamma(double x);
double complex my_cgamma(double complex x);
// equivalent to `log(gamma(x))`
double my_lgamma(double x);
double complex my_lcgamma(double complex x);
```

we would like this to be self contained, and distributable to any other C99 project. further, the results should be accurate to the requested precision (`double`

in C is typically IEEE 64 bit)

we include `lgamma`

functions to compute the logarithm of the gamma function; we won’t go into optimizing for this case too much, but i need this for kscript so we will also generate it (to generate it yourself, include `--lgamma`

in your arguments to the script)

## Implementation

the source code i used is available for free: view on GitHub

i used this paper to form the basis of my implementation for the Zeta function. specifically, section `1.2`

entitled `Convergence of alternating series method`

. we’ll also need an implementation of the Gamma Function, which i’ve linked papers to help us. note that we can use C’s `tgamma`

function for real number computations, but we’ll have to roll our own for complex numbers (we’ll implement both, for completeness). i won’t go into all of the derivations for all the formula (those are covered in the papers i linked if you’re interested); i’ll try and just breifly cover the motivation and basic algebra between formulas

references:

- 0: Numerical Evaluation of the Riemann Zeta Function
- 1: Lanczos approximation
- 2: Lanczos approximation (mrob.com)
- 3: An Analysis of the Lanczos Gamma Approximation

we will dynamically generate C99 code that will be suitable to machine precision via a `Python`

script, which will use the Python package `gmpy2`

(`pip3 install gmpy2`

)

we start by importing things, declaring an argument format, and having some built ins:

```
# std library
import sys
import math
import argparse
# for cached functions (may help performance)
from functools import lru_cache
# gmpy2: multiprecision
import gmpy2
from gmpy2 import mpfr, const_pi, exp, sqrt, log, log10
# add commandline arguments
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=40))
parser.add_argument('--prec', help='Internal precision to use in all computations (in bits)', default=1024, type=int)
parser.add_argument('--prefix', help='Prefix to the C style functions to generate (include the "_"!)', default="my_")
parser.add_argument('--lgamma', help='Whether or not to include an implementation of the `lgamma` function', action='store_true')
args = parser.parse_args()
# set precision to the requested one
gmpy2.get_context().precision = args.prec
# pi & e, but to full precision within gmpy2
pi = const_pi()
e = exp(1)
# factorial, product of all integers <= x
@lru_cache
def factorial(x):
return math.factorial(x)
# double factorial, if n is even, product of all even numbers <= n, otherwise the product of all odd numbers <= n
@lru_cache
def double_factorial(n):
if n <= 1:
return 1
else:
return n * double_factorial(n - 2)
# n choose k, i.e.
@lru_cache
def choose(n, k):
if k < 0:
return 0
return math.comb(n, k)
# return the amount of digits that are accurate in a given error bound
# example: digits_accurate(.001) returns 3, since it is down to 3 places
def digits_accurate(x):
return float(max([-log10(x), 0]))
```

these are all pretty self explanatory; especially with the comments. we use `@lru_cache`

to cache results of functions to reduce overhead when repeatedly calling with the same arguments.

## Gamma Function

to create a table-based approximation (specifically, the Lanczos Approximation), we start with an observation that the Gamma function can be written as:

\[\Gamma(x+1) \approx \sqrt{2 \pi} (x+g+\frac{1}{2})^{x+\frac{1}{2}} e^{-(x+g+\frac{1}{2})} A_g(x)\]where

\[A_g(x) = \frac{1}{2}p_0(g) + p_1(g)\frac{x}{x+1} + p_2(g)\frac{x(x-1)}{(x+1)(x+2)} + ...\]where $g$ is a specifically chosen number to maximize accuracy, $n$ is the number of terms, and the $p_i$ are coefficients computed for the best fit

this works fine on paper, but when actually implementing the C code there is a number of problems:

- the number of floating-point multiplies is $O(n^2)$ at run time (think about all the fractions in $A_g$), which will make it slower to execute, as well as introduce more error
- those multiplications will result in fractions which have very different magnitudes; doing that and summing their results will make roundoff errors much more prevalent
- more intermediate adds $x, x+1, x+2, …$, means more register usage and for large tables, maybe even stack variables (that’s really bad for performance!)

Wouldn’t it be nice to re-arrange the $A_g$ function into something that looks like the following:

\[A_g'(x) = a_0 + \frac{a_1}{x+1} + \frac{a_2}{x+2} + ... \frac{a_{n-1}}{x+n-1}\]Therefore minimizing and simplifying the resulting code? Yes it would! But how do we do that?

Well; we view the summation as a Partial Fraction Summation; think of the following equation:

\[\frac{1}{x+1} + \frac{1}{x+2} = \frac{(x + 1) + (x + 2)}{(x+1)(x+2)} = \frac{2x+3}{(x+1)(x+2)}\]We can algebraically manipulate the rational expression to yield either a summation of divisions, or a division of products & sums. This is actually very similar to our above expression that we want for $A_g’(x)$

I’ll skip all the murky details here, but essentially we’ll end up solving a matrix equation that will tell us what our $a_i$ should be (similar to how, in the simple expression, if we are given $\frac{2x+3}{(x+1)(x+2)}$, our result $a_i$ should be $[1, 1]$). That matrix equation is defined via:

\[a = \mathbf{B} \mathbf{C} \mathbf{D_c} \mathbf{D_r} p\]Where $a, p$ are vectors of the coefficients mentioned in the above formulas, and the rest are $n \times n$ matrices generated to describe the partial fraction decomposition.

Further, the error may be calculated as:

\[\textrm{err} = |\Gamma(x) - approx(x)| \leq |\frac{\pi}{2}(\frac{e^g}{\sqrt{2}} - (\frac{p_0}{2} + \sum_{j=1}^{n}(-1)^{j} p_j))|\]We would like to ensure that \(\textrm{err} \leq 10^{-14}\), which is among the limits of IEEE 64 bit floating point (i.e. a `double`

in C)

Here’s my code implementing the actual formulae:

```
# generates a table for the Gamma function, used for approximation
# returns (coefs, errbound())
@lru_cache
def get_gamma_table(n, g):
# we need to generate the array of coefficients `a` such that:
# Gamma(x) = (x + g + 0.5)^(x+0.5) / (e^(x+g-0.5)) * L_g(x)
# L_g(x) = a[0] + sum(a[k] / (z + k) for k in range(1, N))
# essentially, we construct some matrices from number-theoretic functions
# and we can generate the coefficients of the partial fraction terms `1 / (z + k)`
# This greatly simplifies from the native `z(z-1).../((z+1)(z+2)...)` form, which
# would require a lot more operations to implement internally (see definition of Ag(z) on wikipedia)
# calculate an element for the 'B' matrix (n x n)
def getB(i, j):
if i == 0:
return 1
elif i > 0 and j >= i:
return (-1) ** (j - i) * choose(i + j - 1, j - i)
else:
return 0
# calculate an element for the 'C' matrix (n x n)
def getC(i, j):
if i == j and i == 0:
return mpfr(0.5)
elif j > i:
return 0
else:
# this is the closed form instead of calculating a sum via the 'S' symbol mentioned in some places
return int((-1) ** (i - j) * 4 ** j * i * factorial(i + j - 1) / (factorial(i - j) * factorial(2 * j)))
# calculate an element for the 'Dc' matrix (n x n)
@lru_cache
def getDc(i, j):
if i != j:
# it's a diagonal matrix, so return 0 for all non-diagonal elements
return 0
else:
# otherwise, compute via the formula given
return 2 * double_factorial(2 * i - 1)
# calculate an element for the 'Dr' matrix (n x n)
@lru_cache
def getDr(i, j):
# it's diagonal, so filter out non-diagonal efforts
if i != j:
return 0
elif i == 0:
return 1
else:
# guaranteed to be a integer, so cast it (so no precision is lost)
return -int(factorial(2 * i) / (2 * factorial(i) * factorial(i - 1)))
# generate matrices from their generator functions as 2D lists
# NOTE: this obviously isn't very efficient, but it allows arbitrary precision elements,
# which numpy does not
# these matrices are size <100, so it won't be that bad anyway
B = [[getB (i, j) for j in range(n)] for i in range(n)]
C = [[getC (i, j) for j in range(n)] for i in range(n)]
Dc = [[getDc(i, j) for j in range(n)] for i in range(n)]
Dr = [[getDr(i, j) for j in range(n)] for i in range(n)]
# the `f` vector, defined as `F` but without the double rising factorial (which Dc has)
# i left this in here instead of combining here to be more accurate to
# the method given in 4
f_gn = [sqrt(2) * (e / (2 * (i + g) + 1)) ** (i + 0.5) for i in range(n)]
# multiply matrices X*Y*...
def matmul(X, Y, *args):
if args:
return matmul(matmul(X, Y), *args)
else:
# nonrecursive
assert len(X[0]) == len(Y)
M, N, K = len(X), len(Y[0]), len(Y)
# GEMM kernel (very inefficient; but doesn't matter due to AP floats & small matrix sizes)
return [[sum(X[i][k] * Y[k][j] for k in range(K)) for j in range(N)] for i in range(M)]
# normalization factor; we multiply everything by this so it is `pretty close` to 1.0
W = exp(g) / sqrt(2 * pi)
# get the resulting coefficients
# NOTE: we should get a column vector back, so return the 0th element of each row to get the coefficients
a = list(map(lambda x: W * x[0], matmul(Dr, B, C, Dc, [[f_gn[i]] for i in range(n)])))
# compute 'p' coefficients (only needed for the error bound function)
p = [sum([getC(2 * j, 2 * j) * f_gn[j] * Dc[j][j] for j in range(i)]) for i in range(n)]
# error bound; does not depend on 'x'
def errbound():
# given: err <= |pi/2*W*( sqrt(pi) - u*a )|
# compute dot product `u * a`
dot_ua = (a[0] + sum([2 * a[i] / mpfr(2 * i - 1) for i in range(1, n)])) / W
# compute full formula
return abs(pi / 2 * W) * abs(sqrt(pi) - dot_ua)
# return them
return a, errbound
```

Great! Now we can generate a function (in `C`

code), that should look like:

I define macros for constants such as `PI`

for kscript; but you may want to use your own; check the script to see how i precompute constants. The script will generate constants for $\pi$, $\log \pi$, $\sqrt{2 \pi}$, $\log\sqrt{2 \pi}$ in full precision, so we don’t have to worry about that (defines them as `MY_PI`

, `MY_LOG_PI`

, etc)

```
// evaluate the gamma function at a given point
double my_gamma(double x) {
if (x <= 0) {
// check for poles
if (x == (int)x) return INFINITY;
// use reflection formula, since it won't converge otherwise
// Gamma(x) = pi / (sin(pi * x) * Gamma(1 - x))
return MY_PI / (sin(MY_PI * x) * my_gamma(1 - x));
} else {
// shift off by 1 to make indexing cleaner
x -= 1.0;
// constant
static const double g = ...;
// length (n) used
static const int a_n = ...;
// array of data
static const long double a[] = {
... table values ...
};
// keep track of sum
long double sum = a[0];
int i;
for (i = 1; i < a_n; ++i) {
sum += a[i] / (x + i);
}
// temporary variable
double tmp = x + g + 0.5;
return sqrt(2 * MY_PI) * pow(tmp, x + 0.5) * exp(-tmp) * sum;
}
}
```

Similarly, we can do the complex version (replacing `sin`

with `csin`

, etc):

```
double complex my_cgamma(double complex x) {
double x_re = creal(x), x_im = cimag(x);
// short circuit for real only
if (x_im == 0.0) return my_gamma(x_re);
if (x_re < 0) {
// use reflection formula, since it won't converge otherwise
// Gamma(x) = pi / (sin(pi * x) * Gamma(1 - x))
return MY_PI / (csin(MY_PI * x) * my_cgamma(1 - x));
} else {
// shift off by 1 to make indexing cleaner
x -= 1.0;
// constant
static const double g = ...;
// length (n) used
static const int a_n = ...;
// array of data
static const long double a[] = {
... table values ...
};
// keep track of sum
long double complex sum = a[0];
int i;
for (i = 1; i < a_n; ++i) {
sum += a[i] / (x + i);
}
// temporary variable
double complex tmp = x + g + 0.5;
return MY_SQRT_2PI * pow(tmp, x + 0.5) * exp(-tmp) * sum;
}
}
```

For the sake of brevity; i won’t post the implementation of `my_*lgamma`

; it is very similar to these. run the script yourself to see it’s output for that!

## Zeta Function

The hard part is over; the Zeta function is actually (in my opinion) easier to generate a table for. it is based on a (quite) simple formula:

\[\zeta(x) = \frac{1}{d_0(1 - 2^{1-x})} \sum_{k=1}^{n}\frac{(-1)^{k-1}d_k}{k^x} + \gamma_n(x)\]Where:

$n$ is the number of terms in the approximation, $d$ is a vector of coefficients (similar to $a$ in the Gamma approximation) best fit for a particular model, and $gamma_n(x)$ is the error term

The form of $d_k$ is much simpler to compute; it is given by:

\[d_k = n \sum_{j=k}^{n}\frac{(n+j-1)!4^j}{(n-j)!(2j)!}\]The error term is bounded by:

\[|\gamma_n(x)| \leq \frac{2}{(3+\sqrt{8})^n|\Gamma(x)||1-2^{1-x}|} \leq \frac{3(1+2|\Im(x)|e^{\frac{|\Im(x)|\pi}{2}})}{(3+\sqrt{8})^n|1-2^{1-x}|}\]And, therefore, again in this approximation, we would require that $ \gamma_n(x) \leq 10^{-14}$, to give us a sufficiently accurate approximation

```
# -- ZETA --
# generates a zeta table which can be used for calculation
# returns (coefs, errbound(x)), which are the coefficients for the table as well as an error bound function
@lru_cache
def get_zeta_table(n):
# the error bound of the approximation technique for a given input `x`
def errbound(x):
# absolute value of the imaginary component
t = abs(complex(x).imag)
# calculate error term
et = (3 / (3 + sqrt(8)) ** n) * ((1 + 2 * t) * exp(t * pi / 2)) / (1 - 2 ** (1 - x))
return abs(et)
# compute `d_k`, from Proposition #1 in the paper
def d(k):
res = 0
for j in range(k, n+1):
num, den = factorial(n + j - 1) * 4 ** j, (factorial(n - j) * factorial(2 * j))
res += mpfr(num) / den
return n * res
# d(0) is the value by which we normalize; this is more efficient
# and reduces loss of precision while doing arithmetic in C
d_norm = d(0)
# list of 'd's to sum later (normalized to d(0))
d = [d(k) / d_norm for k in range(1, n + 1)]
return d, errbound
```

to generate C code for real inputs, we can generate a single table (since, the errbound() function relies on the imaginary component of the input, and does not change significantly with the real portion).

```
double my_zeta(double x) {
if (x < 0) {
// check for negative even integers (which are exactly 0)
int ix = (int)x;
if (ix == x && ix % 2 == 0) return 0.0;
// use reflection formula with the functional equation
// Zeta(x) = 2 * (2*PI)^(x-1) * sin(x * PI/2) * Gamma(1-x) * Zeta(1 - x)
return 2 * pow(2 *MY_PI, x - 1) * sin(x * MY_PI / 2) * my_gamma(1 - x) * my_zeta(1 - x);
} else {
// for x >= 0, summation with the coefficients will work fine
// the 'n' used in computation
static const int n = ...;
// d_k for k == 0 through n-1 (0-indexing)
// NOTE: we bake in the alternating sign here, instead of at runtime (cheaper this way)
static const long double d[] = {
... table values ...
};
// use `long double` will prevent some rounding errors
long double sum = 0.0;
int i;
// compute elementwise sum
for (i = 0; i < n; ++i) {
sum += d[i] * pow(i + 1, -x);
}
// divide by the normalization factor in Proposition 1 (without d0, since everything has been normalized by that)
sum /= 1 - pow(2, 1 - x);
return (double)sum;
}
}
```

Note that our zeta function may call the gamma function we defined; this is neccessary due to the fact that the summation will not converge for negative values of $x$

For complex inputs; we need to realize that the maximum error term increases as the imaginary component; for example, the author estimates that it is required that $n \geq 1.3d + 0.9\Im(x)$, if $d$ digits are required for accuracy. However; for $x$ with small imaginary components, we should use the smallest $n$ that will work:

```
# set to whatever the required digits of precision is
goal_digits = 14.0
# dictiorary where `k, v` indicates:
# when imag(x) <= k, `v` may be used as the list of `d_k` for sufficient accuracy
imag_d_map = { }
# current table length
n = 4
# get table bounds
d, errbound = get_zeta_table(n)
# find tables for imag(x) <= 2 ** p
for p in range(0, 5):
val_r = 2.0
val_i = 2 ** p
while digits_accurate(errbound(val_r + val_i * 1j)) < goal_digits:
n += 4
d, errbound = get_zeta_table(n)
imag_d_map[val_i] = d
```

This code creates a mapping that maps a threshold of the imaginary component to the corresponding minimal table size (and always has $n \equiv 0 \mod 4$), we then loop in our python script to generate the following generated code:

```
double complex my_czeta(double complex x) {
// get real and imaginary components
double x_re = creal(x), x_im = cimag(x);
// use the real-only version of the function if it is a real argument
if (x_im == 0.0) return my_zeta(x_re);
if (x_re < 0) {
// use reflection formula with the functional equation
// Zeta(x) = 2 * (2*PI)^(x-1) * sin(x * PI/2) * Gamma(1-x) * Zeta(1 - x)
return 2 * cpow(2 * MY_PI, x - 1) * csin(x * MY_PI / 2) * my_cgamma(1 - x) * my_czeta(1 - x);
} else {
// when the real component is >= 0, summation with the coefficients will work fine
/* Generated Tables */
// the 'n' used in computation
// NOTE: this table is useful for abs(imag(x)) <= 1
static const int n_0 = 28;
// d_k for k == 0 through n-1 (0-indexing)
// NOTE: we bake in the alternating sign here, instead of at runtime (cheaper this way)
static const long double d_0[] = {
... table values ...
};
// the 'n' used in computation
// NOTE: this table is useful for abs(imag(x)) <= 2
static const int n_1 = 28;
// d_k for k == 0 through n-1 (0-indexing)
// NOTE: we bake in the alternating sign here, instead of at runtime (cheaper this way)
static const long double d_1[] = {
... table values ...
};
// the 'n' used in computation
// NOTE: this table is useful for abs(imag(x)) <= 4
static const int n_2 = 28;
// d_k for k == 0 through n-1 (0-indexing)
// NOTE: we bake in the alternating sign here, instead of at runtime (cheaper this way)
static const long double d_2[] = {
... table values ...
};
// the 'n' used in computation
// NOTE: this table is useful for abs(imag(x)) <= 16
static const int n_4 = 40;
// d_k for k == 0 through n-1 (0-indexing)
// NOTE: we bake in the alternating sign here, instead of at runtime (cheaper this way)
static const long double d_4[] = {
... table values ...
};
// absolute value of the imaginary portion (used for discriminating amongst tables)
double x_im_abs = fabs(x_im);
// the sum of all elements in the series
long double complex sum = 0.0;
int i;
if (x_im_abs <= 1) {
for (i = 0; i < n_0; ++i) {
sum += d_0[i] * cpow(i + 1, -x);
}
} else if (x_im_abs <= 2) {
for (i = 0; i < n_1; ++i) {
sum += d_1[i] * cpow(i + 1, -x);
}
} else if (x_im_abs <= 4) {
for (i = 0; i < n_2; ++i) {
sum += d_2[i] * cpow(i + 1, -x);
}
} else if (x_im_abs <= 8) {
for (i = 0; i < n_3; ++i) {
sum += d_3[i] * cpow(i + 1, -x);
}
} else {
for (i = 0; i < n_4; ++i) {
sum += d_4[i] * cpow(i + 1, -x);
}
}
// transform by the normalization factor
sum /= 1 - cpow(2, 1 - x);
return sum;
}
}
```

The real code goes up to $2^{10}$, but you get the point; essentially, the smallest possible table is generated for every power of two threshold of the imaginary component. For example, $n = 144$ is required for $\Im(x) \leq 128$. If we used that for all numbers, we would be $\frac{144}{28} \approx 5.14$ times slower than we need to be! This will help in performance.

## Testing

To test this, i wrote a small program using the generated code. You can check out the full source code (mg.c). I tested values of $x=\sigma+it$, for $\sigma, t \in [0, 256)$, and compared the time. I also compared the built in `tgamma`

function discussed earlier, and measured how accurate my implementation was relative to it; here are the results summarized:

```
gcc -std=c99 -Ofast -fno-math-errno t.c -lm -o test_gz
./test_gz
out:# -- ACCURACY
out:|my_gamma(x)-tgamma(x)| <= 0.000000 , at x=0.000480, accurate to 14.76 digits
out:
out:# -- SPEED
out:my_gamma(x), x in [0, 1) : 0.039 us/iter
out:my_gamma(x), x in [0, 4) : 0.038 us/iter
out:my_gamma(x), x in [0, 16) : 0.037 us/iter
out:my_gamma(x), x in [0, 256) : 0.052 us/iter
out:my_cgamma(x), x in [0i, 1i) : 0.137 us/iter
out:my_cgamma(x), x in [0i, 4i) : 0.135 us/iter
out:my_cgamma(x), x in [0i, 16i) : 0.139 us/iter
out:my_cgamma(x), x in [0i, 256i) : 0.155 us/iter
out:my_zeta(x), x in [0, 1) : 0.395 us/iter
out:my_zeta(x), x in [0, 4) : 0.396 us/iter
out:my_zeta(x), x in [0, 16) : 0.397 us/iter
out:my_zeta(x), x in [0, 256) : 0.416 us/iter
out:my_czeta(x), x in [0i, 1i) : 1.356 us/iter
out:my_czeta(x), x in [0i, 4i) : 1.494 us/iter
out:my_czeta(x), x in [0i, 16i) : 2.005 us/iter
out:my_czeta(x), x in [0i, 256i) : 11.456 us/iter
out:tgamma(x), x in [0, 1) : 0.029 us/iter
out:tgamma(x), x in [0, 4) : 0.036 us/iter
out:tgamma(x), x in [0, 16) : 0.058 us/iter
out:tgamma(x), x in [0, 256) : 0.050 us/iter
```

Feel free to compile it on your machine and email me results; i’d be happy to include them.

My implementation and glibc’s implementation of the gamma function agree everywhere up to `14`

digits, which is plenty accurate (we could check Wolfram alpha exactly to see whether i was closer or they were, but they are both fine for our purposes).

The generated source code i use in kscript (as well as for the demo) is available here, as a single file, feel free to use in non-commercial projects.

I hope you’ve enjoyed the blog, and can use these implementations for your own project. The C code is very simple and should be pretty easy to port to other languages (JavaScript, Python, C#, etc, etc).

Thanks for reading!