ulong_extras.h – arithmetic and numbertheoretic functions for singleword integers¶
This module implements functions for single limb unsigned integers, including arithmetic with a precomputed inverse and modular arithmetic.
The module includes functions for square roots, factorisation and primality testing. Almost all the functions in this module are highly developed and extremely well optimised.
The basic type is the ulong
as defined by GMP. Functions
which take a precomputed inverse either have the suffix preinv
and
take an ulong
precomputed inverse as computed by
n_preinvert_limb
or have the suffix _precomp
and accept a
double
precomputed inverse as computed by
n_precompute_inverse
.
Sometimes three functions with similar names are provided for the same
task, e.g. n_mod_precomp
, n_mod2_precomp
and
n_mod2_preinv
. If the part of the name that designates the
functionality ends in 2 then the function has few if any limitations
on its inputs. Otherwise the function may have limitations such as
being limited to 52 or 53 bits. In practice we found that the preinv
functions are generally faster anyway, so most times it pays to just
use the n_blah2_preinv
variants.
Some functions with the n_ll_
or n_lll_
prefix accept
parameters of two or three limbs respectively.
Simple example¶
The following example computes \(ab \pmod{n}\) using a precomputed inverse, where \(a = 12345678, b = 87654321\) and \(n = 111111111\).
#include <stdio.h>
#include "ulong_extras.h"
int main()
{
ulong r, a, b, n, ninv;
a = UWORD(12345678);
b = UWORD(87654321);
n = UWORD(111111111);
ninv = n_preinvert_limb(n);
r = n_mulmod2_preinv(a, b, n, ninv);
flint_printf("%wu*%wu mod %wu is %wu\n", a, b, n, r);
}
The output is:
12345678*87654321 mod 111111111 is 23456790
Random functions¶

ulong n_randlimb(flint_rand_t state)¶
Returns a uniformly pseudo random limb.
The algorithm generates two random half limbs \(s_j\), \(j = 0, 1\), by iterating respectively \(v_{i+1} = (v_i a + b) \bmod{p_j}\) for some initial seed \(v_0\), randomly chosen values \(a\) and \(b\) and
p_0 = 4294967311 = nextprime(2^32)
on a 64bit machine andp_0 = nextprime(2^16)
on a 32bit machine andp_1 = nextprime(p_0)
.

ulong n_randbits(flint_rand_t state, unsigned int bits)¶
Returns a uniformly pseudo random number with the given number of bits. The most significant bit is always set, unless zero is passed, in which case zero is returned.

ulong n_randtest_bits(flint_rand_t state, int bits)¶
Returns a uniformly pseudo random number with the given number of bits. The most significant bit is always set, unless zero is passed, in which case zero is returned. The probability of a value with a sparse binary representation being returned is increased. This function is intended for use in test code.

ulong n_randint(flint_rand_t state, ulong limit)¶
Returns a uniformly pseudo random number up to but not including the given limit. If zero is passed as a parameter, an entire random limb is returned.

ulong n_urandint(flint_rand_t state, ulong limit)¶
Returns a uniformly pseudo random number up to but not including the given limit. If zero is passed as a parameter, an entire random limb is returned. This function provides somewhat better randomness as compared to
n_randint()
, especially for larger values of limit.

ulong n_randtest(flint_rand_t state)¶
Returns a pseudo random number with a random number of bits, from \(0\) to
FLINT_BITS
. The probability of the special values \(0\), \(1\),COEFF_MAX
andWORD_MAX
is increased as is the probability of a value with sparse binary representation. This random function is mainly used for testing purposes. This function is intended for use in test code.

ulong n_randtest_not_zero(flint_rand_t state)¶
As for
n_randtest()
, but does not return \(0\). This function is intended for use in test code.

ulong n_randprime(flint_rand_t state, ulong bits, int proved)¶
Returns a random prime number
(proved = 1)
or probable prime(proved = 0)
withbits
bits, wherebits
must be at least 2 and at mostFLINT_BITS
.

ulong n_randtest_prime(flint_rand_t state, int proved)¶
Returns a random prime number
(proved = 1)
or probable prime(proved = 0)
with size randomly chosen between 2 andFLINT_BITS
bits. This function is intended for use in test code.
Basic arithmetic¶

ulong n_pow(ulong n, ulong exp)¶
Returns
n^exp
. No checking is done for overflow. The exponent may be zero. We define \(0^0 = 1\).The algorithm simply uses a for loop. Repeated squaring is unlikely to speed up this algorithm.

ulong n_flog(ulong n, ulong b)¶
Returns \(\lfloor\log_b n\rfloor\).
Assumes that \(n \geq 1\) and \(b \geq 2\).
Miscellaneous¶
Basic arithmetic with precomputed inverses¶

ulong n_preinvert_limb_prenorm(ulong n)¶
Computes an approximate inverse
invxl
of the limbxl
, with an implicit leading~`1`. More formally it computes:invxl = (B^2  B*x  1)/x = (B^2  1)/x  B
Note that \(x\) must be normalised, i.e. with msb set. This inverse makes use of Lemma 8.1 in [GraMon1994]:
Let \(d\) be normalised, \(d < B\), i.e. it fits in a word, and suppose that \(m d < B^2 \leq (m+1) d\). Let \(0 \leq n \leq B d  1\). Write \(n = n_2 B + n_1 B/2 + n_0\) with \(n_1 = 0\) or \(1\) and \(n_0 < B/2\). Suppose \(q_1 B + q_0 = n_2 B + (n_2 + n_1) (m  B) + n_1 (dB/2) + n_0\) and \(0 \leq q_0 < B\). Then \(0 \leq q_1 < B\) and \(0 \leq n  q_1 d < 2 d\).
In the theorem, \(m\) is the inverse of \(d\). If we let
m = invxl + B
and \(d = x\) we have \(m d = B^2  1 < B^2\) and \((m+1) x = B^2 + d  1 \geq B^2\).The theorem is often applied as follows: note that \(n_0\) and \(n_1 (dB/2)\) are both less than \(B/2\). Also note that \(n_1 (mB) < B\). Thus the sum of all these terms contributes at most \(1\) to \(q_1\). We are left with \(n_2 B + n_2 (mB)\). But note that \((mB)\) is precisely our precomputed inverse
invxl
. If we write \(q_1 B + q_0 = n_2 B + n_2 (mB)\), then from the theorem, we have \(0 \leq n  q_1 d < 3 d\), i.e. the quotient is out by at most \(2\) and is always either correct or too small.

ulong n_preinvert_limb(ulong n)¶
Returns a precomputed inverse of \(n\), as defined in [GraMol2010]. This precomputed inverse can be used with all of the functions that take a precomputed inverse whose names are suffixed by
_preinv
.We require \(n > 0\).

double n_precompute_inverse(ulong n)¶
Returns a precomputed inverse of \(n\) with double precision value \(1/n\). This precomputed inverse can be used with all of the functions that take a precomputed inverse whose names are suffixed by
_precomp
.We require \(n > 0\).

ulong n_mod_precomp(ulong a, ulong n, double ninv)¶
Returns \(a \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_precompute_inverse()
. We requiren < 2^FLINT_D_BITS
anda < 2^(FLINT_BITS1)
and \(0 \leq a < n^2\).We assume the processor is in the standard round to nearest mode. Thus
ninv
is correct to \(53\) binary bits, the least significant bit of which we shall call a place, and can be at most half a place out. When \(a\) is multiplied by \(ninv\), the binary representation of \(a\) is exact and the mantissa is less than \(2\), thus we see thata * ninv
can be at most one out in the mantissa. We now truncatea * ninv
to the nearest integer, which is always a round down. Either we already have an integer, or we need to make a change down of at least \(1\) in the last place. In the latter case we either get precisely the exact quotient or below it as when we rounded the product to the nearest place we changed by at most half a place. In the case that truncating to an integer takes us below the exact quotient, we have rounded down by less than \(1\) plus half a place. But as the product is less than \(n\) and \(n\) is less than \(2^{53}\), half a place is less than \(1\), thus we are out by less than \(2\) from the exact quotient, i.e. the quotient we have computed is the quotient we are after or one too small. That leaves only the case where we had to round up to the nearest place which happened to be an integer, so that truncating to an integer didn’t change anything. But this implies that the exact quotient \(a/n\) is less than \(2^{54}\) from an integer. We deal with this rare case by subtracting 1 from the quotient. Then the quotient we have computed is either exactly what we are after, or one too small.

ulong n_mod2_precomp(ulong a, ulong n, double ninv)¶
Returns \(a \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_precompute_inverse()
. There are no restrictions on \(a\) or on \(n\).As for
n_mod_precomp()
for \(n < 2^{53}\) and \(a < n^2\) the computed quotient is either what we are after or one too large or small. We deal with these cases. Otherwise we can be sure that the top \(52\) bits of the quotient are computed correctly. We take the remainder and adjust the quotient by multiplying the remainder byninv
to compute another approximate quotient as permod_precomp()
. Now the remainder may be either negative or positive, so the quotient we compute may be one out in either direction.

ulong n_divrem2_preinv(ulong *q, ulong a, ulong n, ulong ninv)¶
Returns \(a \bmod{n}\) and sets \(q\) to the quotient of \(a\) by \(n\), given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
. There are no restrictions on \(a\) and the only restriction on \(n\) is that it be nonzero.This uses the algorithm of Granlund and Möller [GraMol2010]. First \(n\) is normalised and \(a\) is shifted into two limbs to compensate. Then their algorithm is applied verbatim and the remainder shifted back.

ulong n_div2_preinv(ulong a, ulong n, ulong ninv)¶
Returns the Euclidean quotient of \(a\) by \(n\) given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
. There are no restrictions on \(a\) and the only restriction on \(n\) is that it be nonzero.This uses the algorithm of Granlund and Möller [GraMol2010]. First \(n\) is normalised and \(a\) is shifted into two limbs to compensate. Then their algorithm is applied verbatim.

ulong n_mod2_preinv(ulong a, ulong n, ulong ninv)¶
Returns \(a \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
. There are no restrictions on \(a\) and the only restriction on \(n\) is that it be nonzero.This uses the algorithm of Granlund and Möller [GraMol2010]. First \(n\) is normalised and \(a\) is shifted into two limbs to compensate. Then their algorithm is applied verbatim and the result shifted back.

ulong n_divrem2_precomp(ulong *q, ulong a, ulong n, double npre)¶
Returns \(a \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_precompute_inverse()
and sets \(q\) to the quotient. There are no restrictions on \(a\) or on \(n\).This is as for
n_mod2_precomp()
with some additional care taken to retain the quotient information. There are also special cases to deal with the case where \(a\) is already reduced modulo \(n\) and where \(n\) is \(64\) bits and \(a\) is not reduced modulo \(n\).

ulong n_ll_mod_preinv(ulong a_hi, ulong a_lo, ulong n, ulong ninv)¶
Returns \(a \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
. There are no restrictions on \(a\), which will be two limbs(a_hi, a_lo)
, or on \(n\).The old version of this function merely reduced the top limb
a_hi
modulo \(n\) so thatudiv_qrnnd_preinv()
could be used.The new version reduces the top limb modulo \(n\) as per
n_mod2_preinv()
and then the algorithm of Granlund and Möller [GraMol2010] is used again to reduce modulo \(n\).

ulong n_lll_mod_preinv(ulong a_hi, ulong a_mi, ulong a_lo, ulong n, ulong ninv)¶
Returns \(a \bmod{n}\), where \(a\) has three limbs
(a_hi, a_mi, a_lo)
, given a precomputed inverse of \(n\) computed byn_preinvert_limb()
. It is assumed thata_hi
is reduced modulo \(n\). There are no restrictions on \(n\).This function uses the algorithm of Granlund and Möller [GraMol2010] to first reduce the top two limbs modulo \(n\), then does the same on the bottom two limbs.

ulong n_mulmod_precomp(ulong a, ulong b, ulong n, double ninv)¶
Returns \(a b \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_precompute_inverse()
. We requiren < 2^FLINT_D_BITS
and \(0 \leq a, b < n\).We assume the processor is in the standard round to nearest mode. Thus
ninv
is correct to \(53\) binary bits, the least significant bit of which we shall call a place, and can be at most half a place out. The product of \(a\) and \(b\) is computed with error at most half a place. Whena * b
is multiplied by \(ninv\) we find that the exact quotient and computed quotient differ by less than two places. As the quotient is less than \(n\) this means that the exact quotient is at most \(1\) away from the computed quotient. We truncate this quotient to an integer which reduces the value by less than \(1\). We end up with a value which can be no more than two above the quotient we are after and no less than two below. However an argument similar to that forn_mod_precomp()
shows that the truncated computed quotient cannot be two smaller than the truncated exact quotient. In other words the computed integer quotient is at most two above and one below the quotient we are after.

ulong n_mulmod2_preinv(ulong a, ulong b, ulong n, ulong ninv)¶
Returns \(a b \bmod{n}\) given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
. There are no restrictions on \(a\), \(b\) or on \(n\). This is implemented by multiplying usingumul_ppmm()
and then reducing usingn_ll_mod_preinv()
.

ulong n_mulmod2(ulong a, ulong b, ulong n)¶
Returns \(a b \bmod{n}\). There are no restrictions on \(a\), \(b\) or on \(n\). This is implemented by multiplying using
umul_ppmm()
and then reducing usingn_ll_mod_preinv()
after computing a precomputed inverse.

ulong n_mulmod_preinv(ulong a, ulong b, ulong n, ulong ninv, ulong norm)¶
Returns \(a b \pmod{n}\) given a precomputed inverse of \(n\) computed by
n_preinvert_limb()
, assuming \(a\) and \(b\) are reduced modulo \(n\) and \(n\) is normalised, i.e. with most significant bit set. There are no other restrictions on \(a\), \(b\) or \(n\).The value
norm
is provided for convenience. As \(n\) is required to be normalised, it may be that \(a\) and \(b\) have been shifted to the left bynorm
bits before calling the function. Their product then has an extra factor of \(2^\text{norm}\). Specifying a nonzeronorm
will shift the product right by this many bits before reducing it.The algorithm used is that of Granlund and Möller [GraMol2010].
Greatest common divisor¶

ulong n_gcd(ulong x, ulong y)¶
Returns the greatest common divisor \(g\) of \(x\) and \(y\). No assumptions are made about the values \(x\) and \(y\).
This function wraps GMP’s
mpn_gcd_1
.

ulong n_gcdinv(ulong *a, ulong x, ulong y)¶
Returns the greatest common divisor \(g\) of \(x\) and \(y\) and computes \(a\) such that \(0 \leq a < y\) and \(a x = \gcd(x, y) \bmod{y}\), when this is defined. We require \(x < y\).
When \(y = 1\) the greatest common divisor is set to \(1\) and \(a\) is set to \(0\).
This is merely an adaption of the extended Euclidean algorithm computing just one cofactor and reducing it modulo \(y\).

ulong n_xgcd(ulong *a, ulong *b, ulong x, ulong y)¶
Returns the greatest common divisor \(g\) of \(x\) and \(y\) and unsigned values \(a\) and \(b\) such that \(a x  b y = g\). We require \(x \geq y\).
We claim that computing the extended greatest common divisor via the Euclidean algorithm always results in cofactor \(\lvert a \rvert < x/2\), \(\lvert b\rvert < x/2\), with perhaps some small degenerate exceptions.
We proceed by induction.
Suppose we are at some step of the algorithm, with \(x_n = q y_n + r\) with \(r \geq 1\), and suppose \(1 = s y_n  t r\) with \(s < y_n / 2\), \(t < y_n / 2\) by hypothesis.
Write \(1 = s y_n  t (x_n  q y_n) = (s + t q) y_n  t x_n\).
It suffices to show that \((s + t q) < x_n / 2\) as \(t < y_n / 2 < x_n / 2\), which will complete the induction step.
But at the previous step in the backsubstitution we would have had \(1 = s r  c d\) with \(s < r/2\) and \(c < r/2\).
Then \(s + t q < r/2 + y_n / 2 q = (r + q y_n)/2 = x_n / 2\).
See the documentation of
n_gcd()
for a description of the branching in the algorithm, which is faster than using division.
Jacobi and Kronecker symbols¶
Modular Arithmetic¶

ulong n_invmod(ulong x, ulong y)¶
Returns the inverse of \(x\) modulo \(y\), if it exists. Otherwise an exception is thrown.
This is merely an adaption of the extended Euclidean algorithm with appropriate normalisation.

ulong n_powmod_precomp(ulong a, slong exp, ulong n, double npre)¶
Returns
a^exp
modulo \(n\) given a precomputed inverse of \(n\) computed byn_precompute_inverse()
. We require \(n < 2^{53}\) and \(0 \leq a < n\). There are no restrictions onexp
, i.e. it can be negative.This is implemented as a standard binary powering algorithm using repeated squaring and reducing modulo \(n\) at each step.

ulong n_powmod_ui_precomp(ulong a, ulong exp, ulong n, double npre)¶
Returns
a^exp
modulo \(n\) given a precomputed inverse of \(n\) computed byn_precompute_inverse()
. We require \(n < 2^{53}\) and \(0 \leq a < n\). The exponentexp
is unsigned and so can be larger than allowed byn_powmod_precomp()
.This is implemented as a standard binary powering algorithm using repeated squaring and reducing modulo \(n\) at each step.

ulong n_powmod(ulong a, slong exp, ulong n)¶
Returns
a^exp
modulo \(n\). We requiren < 2^FLINT_D_BITS
and \(0 \leq a < n\). There are no restrictions onexp
, i.e. it can be negative.This is implemented by precomputing an inverse and calling the
precomp
version of this function.

ulong n_powmod2_preinv(ulong a, slong exp, ulong n, ulong ninv)¶
Returns
(a^exp) % n
given a precomputed inverse of \(n\) computed byn_preinvert_limb()
. We require \(0 \leq a < n\), but there are no restrictions on \(n\) or onexp
, i.e. it can be negative.This is implemented as a standard binary powering algorithm using repeated squaring and reducing modulo \(n\) at each step.
If
exp
is negative but \(a\) is not invertible modulo \(n\), an exception is raised.

ulong n_powmod2(ulong a, slong exp, ulong n)¶
Returns
(a^exp) % n
. We require \(0 \leq a < n\), but there are no restrictions on \(n\) or onexp
, i.e. it can be negative.This is implemented by precomputing an inverse limb and calling the
preinv
version of this function.If
exp
is negative but \(a\) is not invertible modulo \(n\), an exception is raised.

ulong n_powmod2_ui_preinv(ulong a, ulong exp, ulong n, ulong ninv)¶
Returns
(a^exp) % n
given a precomputed inverse of \(n\) computed byn_preinvert_limb()
. We require \(0 \leq a < n\), but there are no restrictions on \(n\). The exponentexp
is unsigned and so can be larger than allowed byn_powmod2_preinv()
.This is implemented as a standard binary powering algorithm using repeated squaring and reducing modulo \(n\) at each step.

ulong n_powmod2_fmpz_preinv(ulong a, const fmpz_t exp, ulong n, ulong ninv)¶
Returns
(a^exp) % n
given a precomputed inverse of \(n\) computed byn_preinvert_limb()
. We require \(0 \leq a < n\), but there are no restrictions on \(n\). The exponentexp
must not be negative.This is implemented as a standard binary powering algorithm using repeated squaring and reducing modulo \(n\) at each step.

ulong n_sqrtmod(ulong a, ulong p)¶
If \(p\) is prime, compute a square root of \(a\) modulo \(p\) if \(a\) is a quadratic residue modulo \(p\), otherwise return \(0\).
If \(p\) is not prime the result is with high probability \(0\), indicating that \(p\) is not prime, or \(a\) is not a square modulo \(p\). Otherwise the result is meaningless.
Assumes that \(a\) is reduced modulo \(p\).

slong n_sqrtmod_2pow(ulong **sqrt, ulong a, slong exp)¶
Computes all the square roots of
a
modulo2^exp
. The roots are stored in an array which is created and whose address is stored in the location pointed to bysqrt
. The array of roots is allocated by the function but must be cleaned up by the user by callingflint_free
. The number of roots is returned by the function. Ifa
is not a quadratic residue modulo2^exp
then 0 is returned by the function and the locationsqrt
points to is set to NULL.

slong n_sqrtmod_primepow(ulong **sqrt, ulong a, ulong p, slong exp)¶
Computes all the square roots of
a
modulop^exp
. The roots are stored in an array which is created and whose address is stored in the location pointed to bysqrt
. The array of roots is allocated by the function but must be cleaned up by the user by callingflint_free
. The number of roots is returned by the function. Ifa
is not a quadratic residue modulop^exp
then 0 is returned by the function and the locationsqrt
points to is set to NULL.

slong n_sqrtmodn(ulong **sqrt, ulong a, n_factor_t *fac)¶
Computes all the square roots of
a
modulom
given the factorisation ofm
infac
. The roots are stored in an array which is created and whose address is stored in the location pointed to bysqrt
. The array of roots is allocated by the function but must be cleaned up by the user by callingflint_free()
. The number of roots is returned by the function. Ifa
is not a quadratic residue modulom
then 0 is returned by the function and the locationsqrt
points to is set to NULL.
Modular Arithmetic with Fixed Operand¶
This is about computing several modular multiplications where one operand and the modulus are fixed, that is, computing \(a b_i \bmod n\) for several \(b_i\)’s and fixed \(a\) and \(n\). Most functions below require \(a < n < 2^{\mathtt{FLINT\_BITS}  1}\) but have no constraint on \(b_i\) (it can be \(\ge n\)).
Some explanations about the method can be found after the functions descriptions. This has been introduced in NTL and is attributed to Victor Shoup. For references, see
NTL code (in particular file FFT.cpp, consulted in NTL v11.5.1);
David Harvey, Faster arithmetic for numbertheoretic transforms, 2014 J.Symbolic Computation (http://dx.doi.org/10.1016/j.jsc.2013.09.002);
Victor Shoup, “Arithmetic Software Libraries”, 2021 (https://doi.org/10.1017/9781108854207.012) (chapter 9 of the book https://doi.org/10.1017/9781108854207).

ulong n_mulmod_precomp_shoup(ulong a, ulong n)¶
Returns
a_precomp
, a scaled approximation of \(a / n\). This requires \(a < n\). Precisely,a_precomp
is the integer \(\lfloor a \cdot 2^{\mathtt{FLINT\_BITS}} / n \rfloor\), and is intended to be used as the precomputed data forn_mulmod_shoup()
, which requires \(n < 2^{\mathtt{FLINT\_BITS}  1}\).

void n_mulmod_precomp_shoup_quo_rem(ulong *a_pr_quo, ulong *a_pr_rem, ulong a, ulong n)¶
Sets
a_pr_quo
to the above scaled approximation of \(\lfloor a \cdot 2^{\mathtt{FLINT\_BITS}} / n \rfloor\), which is the quotient in the integer division of \(a \cdot 2^{\mathtt{FLINT\_BITS}}\) by \(n\), and also setsa_pr_rem
to the remainder in this division. This requires \(a < n\) and is intended to be used as the precomputed data forn_mulmod_and_precomp_shoup()
, which requires \(n < 2^{\mathtt{FLINT\_BITS}  1}\).

ulong n_mulmod_precomp_shoup_rem_from_quo(ulong a_pr_quo, ulong n)¶
Returns the remainder in the integer division of \(a \cdot 2^{\mathtt{FLINT\_BITS}}\) by \(n\). This requires \(a < n\) and is intended to be used as the precomputed data for
n_mulmod_and_precomp_shoup()
, which requires \(n < 2^{\mathtt{FLINT\_BITS}  1}\). This is faster thann_mulmod_precomp_shoup_quo_rem()
whena_pr_quo
is already known.

ulong n_mulmod_shoup(ulong a, ulong b, ulong a_precomp, ulong n)¶
Returns \(a b \bmod n\) given
a_precomp
, a precomputed scaled approximation of \(a / n\) equal to \(\lfloor a \cdot 2^{\mathtt{FLINT\_BITS}} / n \rfloor\). This requires \(n < 2^{\mathtt{FLINT\_BITS}  1}\) and \(a < n\), there is no restriction on \(b\). Works faster than othermulmod
routines (e.g.n_mulmod2_preinv()
) in situations where one repeats modular multiplications with fixed \(a\) and \(n\) and several values of \(b\)’s, which amortizes the time for precomputinga_precomp
. Examples are scalar multiplication of vectors such as_nmod_vec_scalar_mul_nmod()
or of matrices such as_nmod_vec_scalar_mul_nmod()
.

void n_mulmod_and_precomp_shoup(ulong *ab, ulong *ab_precomp, ulong a, ulong b, ulong a_pr_quo, ulong a_pr_rem, ulong b_precomp, ulong n)¶
Sets
ab
to \(a b \bmod n\) and setsab_precomp
to the precomputed scaled approximation forab / n
, that is, \(\lfloor (ab \bmod n) \cdot 2^{\mathtt{FLINT\_BITS}} / n \rfloor\). This requires \(n < 2^{\mathtt{FLINT\_BITS}  1}\), \(a < n\), and \(b < n\). The inputa_pr_quo
anda_pr_rem
is as in the description ofn_mulmod_precomp_shoup_quo_rem()
, andb_precomp
is as in the description ofn_mulmod_precomp_shoup()
. This can be used for example when seeking a list of powers \([a \bmod n, a^2 \bmod n, a^3 \bmod n, \ldots]\) along with associated precomputed data to speed up repeated modular multiplications by these fixed powers.
Here are some explanations. Let \(B = \mathtt{FLINT\_BITS}\), and \(W = 2^B\). We have as input \(a, b, n\), and the goal is mainly to output \(ab \bmod n\). Constraints are: \(a < n\), and \(n\) has \(< B\) bits, i.e. \(0 \le a < n < 2^{B1}\). There is no restriction on b.
This is intended for repeated multiplications with fixed \(a\) and \(n\), and varying \(b\); hence the initial step is seen as a precomputation (it depends on \(a\) and \(n\) only).
Precomputation step: The main function is n_mulmod_precomp_shoup
,
which computes \(\mathtt{a\_precomp} = \lfloor a W / n \rfloor\). The requirement
\(a < n\) ensures a_precomp
fits in a word (i.e. \(\mathtt{a\_precomp} < W\)).
The variant ending in _quo_rem
computes the same quotient
\(\mathtt{a\_pr\_quo} = \mathtt{a\_precomp}\) but also stores the remainder
\(\mathtt{a\_pr\_rem}\) in the division of \(a W\) by \(n\); the variant ending in
_rem_from_quo
deduces the remainder from the quotient if the latter is
already known.
Modular multiplication: The main function is n_mulmod_shoup
, which
takes \(a, b, \hat{a}, n\) as input and returns \(ab \bmod n\), where \(\hat{a} =
\mathtt{a\_precomp}\). The steps are:
find \(\mathtt{p_hi}, \mathtt{p_lo}\) such that \(\hat{a} b = \mathtt{p_hi} W + \mathtt{p_lo}\) (high part of a doubleword multiplication, \(\mathtt{p_lo}\) will not be used)
compute \(c = ab  \mathtt{p_hi} n\) (two singleword multiplications)
if \(c \ge n\), return \(cn\), else return \(c\)
Step 1. One has \(\mathtt{p_hi} = \lfloor ab/n \rfloor\) or \(\mathtt{p_hi} = \lfloor ab/n \rfloor  1\).
Proof: Write \(aW = \hat{a} n + r\), with \(0 \le r < n\). Thus \(\frac{\hat{a} b}{W} = \frac{ab}{n}  \frac{rb}{nW}\). So \(\mathtt{p_hi} = \lfloor \frac{\hat{a} b}{W} \rfloor = \lfloor \frac{ab}{n}  \frac{rb}{nW} \rfloor\). Clearly \(\mathtt{p_hi} \le \lfloor \frac{ab}{n} \rfloor\). And \(rb < nW\) (since \(r < n\) and \(b < W\)) so \(\frac{rb}{nW} > 1\), hence \(\mathtt{p_hi} \ge \lfloor \frac{ab}{n} \rfloor  1\).
Step 2. It follows that either \(c = ab \bmod n\) or \(c = (ab \bmod n) + n\).
This is where the restriction on \(n\) comes into play: allowing \(n\) to have \(B\) bits prevents us from detecting which case we are in (a comparison such as \(c \ge n\) would not tell). Since \(n\) has \(< B\) bits, \(c \ge n\) if and only if \(c = (ab \bmod n) + n\).
Step 3. we use this to detect which case we are in and correct the possible excess.
Combining precomputation and multiplication: The main function is
n_mulmod_and_precomp_shoup
. The goal is to compute not only \(ab \bmod n\),
but also the corresponding quotient \(\mathtt{ab\_precomp} = \lfloor (ab \bmod
n) \cdot W / n \rfloor\). For this we require as input both the precomputed
quotient and remainder for \(a\) (that is, a_pr_quo
and a_pr_rem
), as
well as the precomputed quotient for b
(b_precomp
). This adds the
constraint that \(b < n\).
To simplify notation, write the integer division \(aW = \hat{a} n + \check{a}\) so that \(\hat{a} = \mathtt{a\_pr\_quo}\) and \(\check{a} = \mathtt{a\_pr\_rem}\), and similarly for \(bW = \hat{b} n + \check{b}\) with \(\hat{b} = \mathtt{b\_precomp}\) (we will not use \(\check{b}\)).
The first three steps follow the abovedescribed approach to compute \(ab \bmod n\) using \(\hat{a}\) (but this time, the lower part of the doubleword multiplication is kept):
find \(d, \hat{c}\) such that \(\hat{a} b = d W + \hat{c}\) (doubleword multiplication)
compute \(c = a b  d n\) (two singleword multiplications)
if \(c \ge n\) then \(c \gets c  n\)
At this stage, \(c = ab \bmod n\), and \(\hat{c} = \hat{a} b \bmod W\). Now suppose we have the quotient \(q = \lfloor \frac{\check{a} b}{n} \rfloor\) in the division of \(\check{a}b\) by \(n\). The claim is that \(\hat{c} + q\) is the sought precomputation quotient for \(ab \bmod n\) up to a multiple of \(W\), that is, \(\hat{c} + q = \lfloor \frac{c W}{n} \rfloor \bmod W\). Indeed, one has \(\lfloor \frac{c W}{n} \rfloor = \lfloor \frac{ab W}{n} \rfloor \bmod W\), and it remains to observe that \(\lfloor \frac{ab W}{n} \rfloor = \lfloor \frac{(\hat{a}n+\check{a}) b}{n} \rfloor = \hat{a} b + \lfloor \frac{\check{a} b}{n} \rfloor = \hat{c} + q \bmod W\).
Therefore the remaining steps consist in computing the quotient \(q\), which can be done via the modular multiplication of \(\check{a}\) by \(b\) using the precomputed quotient for \(b\):
find \(q,e\) such that \(\hat{b} \check{a} = q W + e\) (highpart of double word multiplication, \(e\) will not be used)
compute \(h = b \check{a}  q n\) (two singleword multiplications)
if \(h \ge n\) then \(q \gets q+1\)
Divisibility testing¶
Prime number generation and counting¶

void n_primes_init(n_primes_t iter)¶
Initialises the prime number iterator
iter
for use.

void n_primes_clear(n_primes_t iter)¶
Clears memory allocated by the prime number iterator
iter
.

ulong n_primes_next(n_primes_t iter)¶
Returns the next prime number and advances the state of
iter
. The first call returns 2.Small primes are looked up from
flint_small_primes
. When this table is exhausted, primes are generated in blocks by callingn_primes_sieve_range()
.

void n_primes_jump_after(n_primes_t iter, ulong n)¶
Changes the state of
iter
to start generating primes after \(n\) (excluding \(n\) itself).

void n_primes_extend_small(n_primes_t iter, ulong bound)¶
Extends the table of small primes in
iter
to contain at least two primes larger than or equal tobound
.

void n_primes_sieve_range(n_primes_t iter, ulong a, ulong b)¶
Sets the block endpoints of
iter
to the smallest and largest odd numbers between \(a\) and \(b\) inclusive, and sieves to mark all odd primes in this range. The iterator state is changed to point to the first number in the sieved range.

void n_compute_primes(ulong num_primes)¶
Precomputes at least
num_primes
primes and theirdouble
precomputed inverses and stores them in an internal cache. Assuming that FLINT has been built with support for threadlocal storage, each thread has its own cache.

const ulong *n_primes_arr_readonly(ulong num_primes)¶
Returns a pointer to a readonly array of the first
num_primes
prime numbers. The computed primes are cached for repeated calls. The pointer is valid until the user callsn_cleanup_primes()
in the same thread.

const double *n_prime_inverses_arr_readonly(ulong n)¶
Returns a pointer to a readonly array of inverses of the first
num_primes
prime numbers. The computed primes are cached for repeated calls. The pointer is valid until the user callsn_cleanup_primes()
in the same thread.

void n_cleanup_primes()¶
Frees the internal cache of prime numbers used by the current thread. This will invalidate any pointers returned by
n_primes_arr_readonly()
orn_prime_inverses_arr_readonly()
.

ulong n_nextprime(ulong n, int proved)¶
Returns the next prime after \(n\). Assumes the result will fit in an
ulong
. If proved is \(0\), i.e. false, the prime is not proven prime, otherwise it is.

ulong n_prime_pi(ulong n)¶
Returns the value of the prime counting function \(\pi(n)\), i.e. the number of primes less than or equal to \(n\). The invariant
n_prime_pi(n_nth_prime(n)) == n
.Currently, this function simply extends the table of cached primes up to an upper limit and then performs a binary search.

void n_prime_pi_bounds(ulong *lo, ulong *hi, ulong n)¶
Calculates lower and upper bounds for the value of the prime counting function
lo <= pi(n) <= hi
. Iflo
andhi
point to the same location, the high value will be stored.This does a table lookup for small values, then switches over to some proven bounds.
The upper approximation is \(1.25506 n / \ln n\), and the lower is \(n / \ln n\). These bounds are due to Rosser and Schoenfeld [RosSch1962] and valid for \(n \geq 17\).
We use the number of bits in \(n\) (or one less) to form an approximation to \(\ln n\), taking care to use a value too small or too large to maintain the inequality.

ulong n_nth_prime(ulong n)¶
Returns the \(n\)th prime number \(p_n\), using the mathematical indexing convention \(p_1 = 2, p_2 = 3, \dotsc\).
This function simply ensures that the table of cached primes is large enough and then looks up the entry.

void n_nth_prime_bounds(ulong *lo, ulong *hi, ulong n)¶
Calculates lower and upper bounds for the \(n\)th prime number \(p_n\) ,
lo <= p_n <= hi
. Iflo
andhi
point to the same location, the high value will be stored. Note that this function will overflow for sufficiently large \(n\).We use the following estimates, valid for \(n > 5\) :
\[\begin{split}p_n & > n (\ln n + \ln \ln n  1) \\ p_n & < n (\ln n + \ln \ln n) \\ p_n & < n (\ln n + \ln \ln n  0.9427) \quad (n \geq 15985)\end{split}\]The first inequality was proved by Dusart [Dus1999], and the last is due to Massias and Robin [MasRob1996]. For a further overview, see http://primes.utm.edu/howmany.shtml .
We bound \(\ln n\) using the number of bits in \(n\) as in
n_prime_pi_bounds()
, and estimate \(\ln \ln n\) to the nearest integer; this function is nearly constant.
Primality testing¶

int n_is_oddprime_small(ulong n)¶
Returns \(1\) if \(n\) is an odd prime smaller than
FLINT_ODDPRIME_SMALL_CUTOFF
. Expects \(n\) to be odd and smaller than the cutoff.This function merely uses a lookup table with one bit allocated for each odd number up to the cutoff.

int n_is_oddprime_binary(ulong n)¶
This function performs a simple binary search through the table of cached primes for \(n\). If it exists in the array it returns \(1\), otherwise \(0\). For the algorithm to operate correctly \(n\) should be odd and at least \(17\).
Lower and upper bounds are computed with
n_prime_pi_bounds()
. Once we have bounds on where to look in the table, we refine our search with a simple binary algorithm, taking the top or bottom of the current interval as necessary.

int n_is_prime_pocklington(ulong n, ulong iterations)¶
Tests if \(n\) is a prime using the Pocklington–Lehmer primality test. If \(1\) is returned \(n\) has been proved prime. If \(0\) is returned \(n\) is composite. However \(1\) may be returned if nothing was proved either way due to the number of iterations being too small.
The most time consuming part of the algorithm is factoring \(n  1\). For this reason
n_factor_partial()
is used, which uses a combination of trial factoring and Hart’s one line factor algorithm [Har2012] to try to quickly factor \(n  1\). Additionally if the cofactor is less than the square root of \(n  1\) the algorithm can still proceed.One can also specify a number of iterations if less time should be taken. Simply set this to
WORD(0)
if this is irrelevant. In most cases a greater number of iterations will not significantly affect timings as most of the time is spent factoring.See https://mathworld.wolfram.com/PocklingtonsTheorem.html for a description of the algorithm.

int n_is_prime_pseudosquare(ulong n)¶
Tests if \(n\) is a prime according to Theorem 2.7 [LukPatWil1996].
We first factor \(N\) using trial division up to some limit \(B\). In fact, the number of primes used in the trial factoring is at most
FLINT_PSEUDOSQUARES_CUTOFF
.Next we compute \(N/B\) and find the next pseudosquare \(L_p\) above this value, using a static table as per https://oeis.org/A002189/b002189.txt .
As noted in the text, if \(p\) is prime then Step 3 will pass. This test rejects many composites, and so by this time we suspect that \(p\) is prime. If \(N\) is \(3\) or \(7\) modulo \(8\), we are done, and \(N\) is prime.
We now run a probable prime test, for which no known counterexamples are known, to reject any composites. We then proceed to prove \(N\) prime by executing Step 4. In the case that \(N\) is \(1\) modulo \(8\), if Step 4 fails, we extend the number of primes \(p_i\) at Step 3 and hope to find one which passes Step 4. We take the test one past the largest \(p\) for which we have pseudosquares \(L_p\) tabulated, as this already corresponds to the next \(L_p\) which is bigger than \(2^{64}\) and hence larger than any prime we might be testing.
As explained in the text, Condition 4 cannot fail if \(N\) is prime.
The possibility exists that the probable prime test declares a composite prime. However in that case an error is printed, as that would be of independent interest.

int n_is_prime(ulong n)¶
Tests if \(n\) is a prime. This first sieves for small prime factors, then simply calls
n_is_probabprime()
. This has been checked against the tables of Feitsma and Galway http://www.cecm.sfu.ca/Pseudoprimes/index2to64.html and thus constitutes a check for primality (rather than just pseudoprimality) up to \(2^{64}\).In future, this test may produce and check a certificate of primality. This is likely to be significantly slower for prime inputs.

int n_is_strong_probabprime_precomp(ulong n, double npre, ulong a, ulong d)¶
Tests if \(n\) is a strong probable prime to the base \(a\). We require that \(d\) is set to the largest odd factor of \(n  1\) and
npre
is a precomputed inverse of \(n\) computed withn_precompute_inverse()
. We also require that \(n < 2^{53}\), \(a\) to be reduced modulo \(n\) and not \(0\) and \(n\) to be odd.If we write \(n  1 = 2^s d\) where \(d\) is odd then \(n\) is a strong probable prime to the base \(a\), i.e. an \(a\)SPRP, if either \(a^d = 1 \pmod n\) or \((a^d)^{2^r} = 1 \pmod n\) for some \(r\) less than \(s\).
A description of strong probable primes is given here: https://mathworld.wolfram.com/StrongPseudoprime.html

int n_is_strong_probabprime2_preinv(ulong n, ulong ninv, ulong a, ulong d)¶
Tests if \(n\) is a strong probable prime to the base \(a\). We require that \(d\) is set to the largest odd factor of \(n  1\) and
npre
is a precomputed inverse of \(n\) computed withn_preinvert_limb()
. We require \(a\) to be reduced modulo \(n\) and nonzero, and \(n\) to be odd.If we write \(n  1 = 2^s d\) where \(d\) is odd then \(n\) is a strong probable prime to the base \(a\) (an \(a\)SPRP) if either \(a^d = 1 \pmod n\) or \((a^d)^{2^r} = 1 \pmod n\) for some \(r\) less than \(s\).
A description of strong probable primes is given here: https://mathworld.wolfram.com/StrongPseudoprime.html

int n_is_probabprime_fermat(ulong n, ulong i)¶
Returns \(1\) if \(n\) is a base \(i\) Fermat probable prime. Requires \(1 < i < n\) and that \(i\) does not divide \(n\).
By Fermat’s Little Theorem if \(i^{n1}\) is not congruent to \(1\) then \(n\) is not prime.

int n_is_probabprime_fibonacci(ulong n)¶
Let \(F_j\) be the \(j\)th element of the Fibonacci sequence \(0, 1, 1, 2, 3, 5, \dotsc\), starting at \(j = 0\). Then if \(n\) is prime we have \(F_{n  (n/5)} = 0 \pmod n\), where \((n/5)\) is the Jacobi symbol.
For further details, see pp. 142 [CraPom2005].
We require that \(n\) is not divisible by \(2\) or \(5\).

int n_is_probabprime_BPSW(ulong n)¶
Implements a Baillie–Pomerance–Selfridge–Wagstaff probable primality test. This is a variant of the usual BPSW test (which only uses strong base2 probable prime and LucasSelfridge tests, see Baillie and Wagstaff [BaiWag1980]).
This implementation makes use of a weakening of the usual BailliePSW test given in [Chen2003], namely replacing the Lucas test with a Fibonacci test when \(n \equiv 2, 3 \pmod{5}\) (see also the comment on page 143 of [CraPom2005]), regarding Fibonacci pseudoprimes.
There are no known counterexamples to this being a primality test.
Up to \(2^{64}\) the test we use has been checked against tables of pseudoprimes. Thus it is a primality test up to this limit.

int n_is_probabprime_lucas(ulong n)¶
For details on Lucas pseudoprimes, see [pp. 143] [CraPom2005].
We implement a variant of the Lucas pseudoprime test similar to that described by Baillie and Wagstaff [BaiWag1980].

int n_is_probabprime(ulong n)¶
Tests if \(n\) is a probable prime. Up to
FLINT_ODDPRIME_SMALL_CUTOFF
this algorithm usesn_is_oddprime_small()
which uses a lookup table.Next it calls
n_compute_primes()
with the maximum table size and uses this table to perform a binary search for \(n\) up to the table limit.Then up to \(1050535501\) it uses a number of strong probable prime tests,
n_is_strong_probabprime_preinv()
, etc., for various bases. The output of the algorithm is guaranteed to be correct up to this bound due to exhaustive tables, described at http://uucode.com/obf/dalbec/alg.html .Beyond that point the BPSW probabilistic primality test is used, by calling the function
n_is_probabprime_BPSW()
. There are no known counterexamples, and it has been checked against the tables of Feitsma and Galway and up to the accuracy of those tables, this is an exhaustive check up to \(2^{64}\), i.e. there are no counterexamples.
Chinese remaindering¶

ulong n_CRT(ulong r1, ulong m1, ulong r2, ulong m2)¶
Use the Chinese Remainder Theorem to return the unique value \(0 \le x < M\) congruent to \(r_1\) modulo \(m_1\) and \(r_2\) modulo \(m_2\), where \(M = m_1 \times m_2\) is assumed to fit a ulong.
It is assumed that \(m_1\) and \(m_2\) are positive integers greater than \(1\) and coprime. It is assumed that \(0 \le r_1 < m_1\) and \(0 \le r_2 < m_2\).
Square root and perfect power testing¶

ulong n_sqrt(ulong a)¶
Computes the integer truncation of the square root of \(a\).
The implementation uses a call to the IEEE floating point sqrt function. The integer itself is represented by the nearest double and its square root is computed to the nearest place. If \(a\) is one below a square, the rounding may be up, whereas if it is one above a square, the rounding will be down. Thus the square root may be one too large in some instances which we then adjust by checking if we have the right value. We also have to be careful when the square of this too large value causes an overflow. The same assumptions hold for a single precision float provided the square root itself can be represented in a single float, i.e. for \(a < 281474976710656 = 2^{46}\).

ulong n_sqrtrem(ulong *r, ulong a)¶
Computes the integer truncation of the square root of \(a\).
The integer itself is represented by the nearest double and its square root is computed to the nearest place. If \(a\) is one below a square, the rounding may be up, whereas if it is one above a square, the rounding will be down. Thus the square root may be one too large in some instances which we then adjust by checking if we have the right value. We also have to be careful when the square of this too large value causes an overflow. The same assumptions hold for a single precision float provided the square root itself can be represented in a single float, i.e. for \(a < 281474976710656 = 2^{46}\).
The remainder is computed by subtracting the square of the computed square root from \(a\).

int n_is_square(ulong x)¶
Returns \(1\) if \(x\) is a square, otherwise \(0\).
This code first checks if \(x\) is a square modulo \(64\), \(63 = 3 \times 3 \times 7\) and \(65 = 5 \times 13\), using lookup tables, and if so it then takes a square root and checks that the square of this equals the original value.

int n_is_perfect_power235(ulong n)¶
Returns \(1\) if \(n\) is a perfect square, cube or fifth power.
This function uses a series of modular tests to reject most non 235powers. Each modular test returns a value from 0 to 7 whose bits respectively indicate whether the value is a square, cube or fifth power modulo the given modulus. When these are logically
AND
ed together, this gives a powerful test which will reject most non235 powers.If a bit remains set indicating it may be a square, a standard square root test is performed. Similarly a cube root or fifth root can be taken, if indicated, to determine whether the power of that root is exactly equal to \(n\).

int n_is_perfect_power(ulong *root, ulong n)¶
If \(n = r^k\), return \(k\) and set
root
to \(r\). Note that \(0\) and \(1\) are considered squares. No guarantees are made about \(r\) or \(k\) being the minimum possible value.

ulong n_rootrem(ulong *remainder, ulong n, ulong root)¶
This function uses the Newton iteration method to calculate the nth root of a number. First approximation is calculated by an algorithm mentioned in this article: https://en.wikipedia.org/wiki/Fast_inverse_square_root . Instead of the inverse square root, the nth root is calculated.
Returns the integer part of
n ^ 1/root
. Remainder is set asn  base^root
. In case \(n < 1\) orroot < 1
, \(0\) is returned.

ulong n_cbrt(ulong n)¶
This function returns the integer truncation of the cube root of \(n\). First approximation is calculated by an algorithm mentioned in this article: https://en.wikipedia.org/wiki/Fast_inverse_square_root . Instead of the inverse square root, the cube root is calculated. This functions uses different algorithms to calculate the cube root, depending upon the size of \(n\). For numbers greater than \(2^{46}\), it uses
n_cbrt_chebyshev_approx()
. Otherwise, it makes use of the iteration, \(x \leftarrow x  (x\cdot x\cdot x  a)\cdot x/(2\cdot x\cdot x\cdot x + a)\) for getting a good estimate, as mentioned in the paper by W. Kahan [Kahan1991] .

ulong n_cbrt_binary_search(ulong n)¶
This function returns the integer truncation of the cube root of \(n\). Uses binary search to get the correct value.

ulong n_cbrt_chebyshev_approx(ulong n)¶
This function returns the integer truncation of the cube root of \(n\). The number is first expressed in the form
x * 2^exp
. This ensures \(x\) is in the range [0.5, 1]. Cube root of x is calculated using Chebyshev’s approximation polynomial for the function \(y = x^{1/3}\). The values of the coefficient are calculated from the Python module mpmath, https://mpmath.org, using the function chebyfit. x is multiplied by2^exp
and the cube root of 1, 2 or 4 (according toexp%3
).
Factorisation¶

void n_factor_init(n_factor_t *factors)¶
Initializes factors.

ulong n_factor_evaluate(const n_factor_t *factors)¶
Returns the evaluation of
factors
, i.e. \(p_{1}^{e_{1}} \cdots p_{n}^{e_{n}}\) assuming that it fits in a limb. In case the evaluation does not fit in a limb, it returns \(0\).

int n_remove(ulong *n, ulong p)¶
Removes the highest possible power of \(p\) from \(n\), replacing \(n\) with the quotient. The return value is the highest power of \(p\) that divided \(n\). Assumes \(n\) is not \(0\).
For \(p = 2\) trailing zeroes are counted. For other primes \(p\) is repeatedly squared and stored in a table of powers with the current highest power of \(p\) removed at each step until no higher power can be removed. The algorithm then proceeds down the power tree again removing powers of \(p\) until none remain.

int n_remove2_precomp(ulong *n, ulong p, double ppre)¶
Removes the highest possible power of \(p\) from \(n\), replacing \(n\) with the quotient. The return value is the highest power of \(p\) that divided \(n\). Assumes \(n\) is not \(0\). We require
ppre
to be set to a precomputed inverse of \(p\) computed withn_precompute_inverse()
.For \(p = 2\) trailing zeroes are counted. For other primes \(p\) we make repeated use of
n_divrem2_precomp()
until division by \(p\) is no longer possible.

void n_factor_insert(n_factor_t *factors, ulong p, ulong exp)¶
Inserts the given prime power factor
p^exp
into then_factor_t
factors
. See the documentation forn_factor_trial()
for a description of then_factor_t
type.The algorithm performs a simple search to see if \(p\) already exists as a prime factor in the structure. If so the exponent there is increased by the supplied exponent. Otherwise a new factor
p^exp
is added to the end of the structure.There is no test code for this function other than its use by the various factoring functions, which have test code.

ulong n_factor_trial_range(n_factor_t *factors, ulong n, ulong start, ulong num_primes)¶
Trial factor \(n\) with the first
num_primes
primes, but starting at the prime with index start (counting from zero).One requires an initialised
n_factor_t
structure, but factors will be added by default to an already usedn_factor_t
. Use the functionn_factor_init()
defined inulong_extras
if initialisation has not already been completed on factors.Once completed,
num
will contain the number of distinct prime factors found. The field \(p\) is an array ofulong
s containing the distinct prime factors,exp
an array containing the corresponding exponents.The return value is the unfactored cofactor after trial factoring is done.
The function calls
n_compute_primes()
automatically. See the documentation for that function regarding limits.The algorithm stops when the current prime has a square exceeding \(n\), as no prime factor of \(n\) can exceed this unless \(n\) is prime.
The precomputed inverses of all the primes computed by
n_compute_primes()
are utilised with then_remove2_precomp()
function.

ulong n_factor_trial(n_factor_t *factors, ulong n, ulong num_primes)¶
This function calls
n_factor_trial_range()
, with the value of \(0\) forstart
. By default this adds factors to an already existingn_factor_t
or to a newly initialised one.

ulong n_factor_power235(ulong *exp, ulong n)¶
Returns \(0\) if \(n\) is not a perfect square, cube or fifth power. Otherwise it returns the root and sets
exp
to either \(2\), \(3\) or \(5\) appropriately.This function uses a series of modular tests to reject most non 235powers. Each modular test returns a value from 0 to 7 whose bits respectively indicate whether the value is a square, cube or fifth power modulo the given modulus. When these are logically
AND
ed together, this gives a powerful test which will reject most non235 powers.If a bit remains set indicating it may be a square, a standard square root test is performed. Similarly a cube root or fifth root can be taken, if indicated, to determine whether the power of that root is exactly equal to \(n\).

ulong n_factor_one_line(ulong n, ulong iters)¶
This implements Bill Hart’s one line factoring algorithm [Har2012]. It is a variant of Fermat’s algorithm which cycles through a large number of multipliers instead of incrementing the square root. It is faster than SQUFOF for \(n\) less than about \(2^{40}\).

ulong n_factor_lehman(ulong n)¶
Lehman’s factoring algorithm. Currently works up to \(10^{16}\), but is not particularly efficient and so is not used in the general factor function. Always returns a factor of \(n\).

ulong n_factor_SQUFOF(ulong n, ulong iters)¶
Attempts to split \(n\) using the given number of iterations of SQUFOF. Simply set
iters
toWORD(0)
for maximum persistence.The version of SQUFOF implemented here is as described by Gower and Wagstaff [GowWag2008].
We start by trying SQUFOF directly on \(n\). If that fails we multiply it by each of the primes in
flint_primes_small
in turn. As this multiplication may result in a two limb value we allow this in our implementation of SQUFOF. As SQUFOF works with values about half the size of \(n\) it only needs single limb arithmetic internally.If SQUFOF fails to factor \(n\) we return \(0\), however with
iters
large enough this should never happen.

void n_factor(n_factor_t *factors, ulong n, int proved)¶
Factors \(n\) with no restrictions on \(n\). If the prime factors are required to be checked with a primality test, one may set
proved
to \(1\), otherwise set it to \(0\), and they will only be probable primes. NB: at the present there is no difference because the probable prime tests have been exhaustively tested up to \(2^{64}\).However, in future, this flag may produce and separately check a primality certificate. This may be quite slow (and probably no less reliable in practice).
For details on the
n_factor_t
structure, seen_factor_trial()
.This function first tries trial factoring with a number of primes specified by the constant
FLINT_FACTOR_TRIAL_PRIMES
. If the cofactor is \(1\) or prime the function returns with all the factors.Otherwise, the cofactor is placed in the array
factor_arr
. Whilst there are factors remaining in there which have not been split, the algorithm continues. At each step each factor is first checked to determine if it is a perfect power. If so it is replaced by the power that has been found. Next if the factor is small enough and composite, in particular, less thanFLINT_FACTOR_ONE_LINE_MAX
thenn_factor_one_line()
is called withFLINT_FACTOR_ONE_LINE_ITERS
to try and split the factor. If that fails or the factor is too large forn_factor_one_line()
thenn_factor_SQUFOF()
is called, withFLINT_FACTOR_SQUFOF_ITERS
. If that fails an error results and the program aborts. However this should not happen in practice.

ulong n_factor_trial_partial(n_factor_t *factors, ulong n, ulong *prod, ulong num_primes, ulong limit)¶
Attempts trial factoring of \(n\) with the first
num_primes primes
, but stops when the product of prime factors so far exceedslimit
.One requires an initialised
n_factor_t
structure, but factors will be added by default to an already usedn_factor_t
. Use the functionn_factor_init()
defined inulong_extras
if initialisation has not already been completed onfactors
.Once completed,
num
will contain the number of distinct prime factors found. The field \(p\) is an array ofulong
s containing the distinct prime factors,exp
an array containing the corresponding exponents.The return value is the unfactored cofactor after trial factoring is done. The value
prod
will be set to the product of the factors found.The function calls
n_compute_primes()
automatically. See the documentation for that function regarding limits.The algorithm stops when the current prime has a square exceeding \(n\), as no prime factor of \(n\) can exceed this unless \(n\) is prime.
The precomputed inverses of all the primes computed by
n_compute_primes()
are utilised with then_remove2_precomp()
function.

ulong n_factor_partial(n_factor_t *factors, ulong n, ulong limit, int proved)¶
Factors \(n\), but stops when the product of prime factors so far exceeds
limit
.One requires an initialised
n_factor_t
structure, but factors will be added by default to an already usedn_factor_t
. Use the functionn_factor_init()
defined inulong_extras
if initialisation has not already been completed onfactors
.On exit,
num
will contain the number of distinct prime factors found. The field \(p\) is an array ofulong
s containing the distinct prime factors,exp
an array containing the corresponding exponents.The return value is the unfactored cofactor after factoring is done.
The factors are proved prime if
proved
is \(1\), otherwise they are merely probably prime.

ulong n_factor_pp1(ulong n, ulong B1, ulong c)¶
Factors \(n\) using Williams’ \(p + 1\) factoring algorithm, with prime limit set to \(B1\). We require \(c\) to be set to a random value. Each trial of the algorithm with a different value of \(c\) gives another chance to factor \(n\), with roughly exponentially decreasing chance of finding a missing factor. If \(p + 1\) (or \(p  1\)) is not smooth for any factor \(p\) of \(n\), the algorithm will never succeed. The value \(c\) should be less than \(n\) and greater than \(2\).
If the algorithm succeeds, it returns the factor, otherwise it returns \(0\) or \(1\) (the trivial factors modulo \(n\)).

ulong n_factor_pp1_wrapper(ulong n)¶
A simple wrapper around
n_factor_pp1
which works in the range \(31\)\(64\) bits. Below this point, trial factoring will always succeed. This function mainly exists forn_factor
and is tuned to minimise the time forn_factor
on numbers that reach then_factor_pp1
stage, i.e. after trial factoring and one line factoring.

int n_factor_pollard_brent_single(ulong *factor, ulong n, ulong ninv, ulong ai, ulong xi, ulong normbits, ulong max_iters)¶
Pollard Rho algorithm (with Brent modification) for integer factorization. Assumes that the \(n\) is not prime. \(factor\) is set as the factor if found. It is not assured that the factor found will be prime. Does not compute the complete factorization, just one factor. Returns 1 if factorization is successful (non trivial factor is found), else returns 0. Assumes \(n\) is normalized (shifted by normbits bits), and takes as input a precomputed inverse of \(n\) as computed by
n_preinvert_limb()
. \(ai\) and \(xi\) should also be shifted left by \(normbits\).\(ai\) is the constant of the polynomial used, \(xi\) is the initial value. \(max\_iters\) is the number of iterations tried in process of finding the cycle.
The algorithm used is a modification of the original Pollard Rho algorithm, suggested by Richard Brent in the paper, available at https://mathspeople.anu.edu.au/~brent/pd/rpb051i.pdf

int n_factor_pollard_brent(ulong *factor, flint_rand_t state, ulong n_in, ulong max_tries, ulong max_iters)¶
Pollard Rho algorithm, modified as suggested by Richard Brent. Makes a call to
n_factor_pollard_brent_single()
. The input parameters ai and xi forn_factor_pollard_brent_single()
are selected at random.If the algorithm fails to find a non trivial factor in one call, it tries again (this time with a different set of random values). This process is repeated a maximum of \(max\_tries\) times.
Assumes \(n\) is not prime. \(factor\) is set as the factor found, if factorization is successful. In such a case, 1 is returned. Otherwise, 0 is returned. Factor discovered is not necessarily prime.
Arithmetic functions¶

int n_moebius_mu(ulong n)¶
Computes the Moebius function \(\mu(n)\), which is defined as \(\mu(n) = 0\) if \(n\) has a prime factor of multiplicity greater than \(1\), \(\mu(n) = 1\) if \(n\) has an odd number of distinct prime factors, and \(\mu(n) = 1\) if \(n\) has an even number of distinct prime factors. By convention, \(\mu(0) = 0\).
For even numbers, we use the identities \(\mu(4n) = 0\) and \(\mu(2n) =  \mu(n)\). Odd numbers up to a cutoff are then looked up from a precomputed table storing \(\mu(n) + 1\) in groups of two bits.
For larger \(n\), we first check if \(n\) is divisible by a small odd square and otherwise call
n_factor()
and count the factors.

void n_moebius_mu_vec(int *mu, ulong len)¶
Computes \(\mu(n)\) for
n = 0, 1, ..., len  1
. This is done by sieving over each prime in the range, flipping the sign of \(\mu(n)\) for every multiple of a prime \(p\) and setting \(\mu(n) = 0\) for every multiple of \(p^2\).
Factorials¶

ulong n_factorial_fast_mod2_preinv(ulong n, ulong p, ulong pinv)¶
Returns \(n! \bmod p\) given a precomputed inverse of \(p\) as computed by
n_preinvert_limb()
. \(p\) is not required to be a prime, but no special optimisations are made for composite \(p\). Uses fast multipoint evaluation, running in about \(O(n^{1/2})\) time.

ulong n_factorial_mod2_preinv(ulong n, ulong p, ulong pinv)¶
Returns \(n! \bmod p\) given a precomputed inverse of \(p\) as computed by
n_preinvert_limb()
. \(p\) is not required to be a prime, but no special optimisations are made for composite \(p\).Uses a lookup table for small \(n\), otherwise computes the product if \(n\) is not too large, and calls the fast algorithm for extremely large \(n\).
Primitive Roots and Discrete Logarithms¶

ulong n_primitive_root_prime_prefactor(ulong p, n_factor_t *factors)¶
Returns a primitive root for the multiplicative subgroup of \(\mathbb{Z}/p\mathbb{Z}\) where \(p\) is prime given the factorisation (
factors
) of \(p  1\).

ulong n_primitive_root_prime(ulong p)¶
Returns a primitive root for the multiplicative subgroup of \(\mathbb{Z}/p\mathbb{Z}\) where \(p\) is prime.

ulong n_discrete_log_bsgs(ulong b, ulong a, ulong n)¶
Returns the discrete logarithm of \(b\) with respect to \(a\) in the multiplicative subgroup of \(\mathbb{Z}/n\mathbb{Z}\) when \(\mathbb{Z}/n\mathbb{Z}\) is cyclic. That is, it returns a number \(x\) such that \(a^x = b \bmod n\). The multiplicative subgroup is only cyclic when \(n\) is \(2\), \(4\), \(p^k\), or \(2p^k\) where \(p\) is an odd prime and \(k\) is a positive integer.
Elliptic curve method for factorization of ulong
¶

void n_factor_ecm_double(ulong *x, ulong *z, ulong x0, ulong z0, ulong n, n_ecm_t n_ecm_inf)¶
Sets the point \((x : z)\) to two times \((x_0 : z_0)\) modulo \(n\) according to the formula
\(x = (x_0 + z_0)^2 \cdot (x_0  z_0)^2 \mod n,\)
\(z = 4 x_0 z_0 \left((x_0  z_0)^2 + 4a_{24}x_0z_0\right) \mod n.\)
This group doubling is valid only for points expressed in Montgomery projective coordinates.

void n_factor_ecm_add(ulong *x, ulong *z, ulong x1, ulong z1, ulong x2, ulong z2, ulong x0, ulong z0, ulong n, n_ecm_t n_ecm_inf)¶
Sets the point \((x : z)\) to the sum of \((x_1 : z_1)\) and \((x_2 : z_2)\) modulo \(n\), given the difference \((x_0 : z_0)\) according to the formula
This group doubling is valid only for points expressed in Montgomery projective coordinates.

void n_factor_ecm_mul_montgomery_ladder(ulong *x, ulong *z, ulong x0, ulong z0, ulong k, ulong n, n_ecm_t n_ecm_inf)¶
Montgomery ladder algorithm for scalar multiplication of elliptic points.
Sets the point \((x : z)\) to \(k(x_0 : z_0)\) modulo \(n\).
Valid only for points expressed in Montgomery projective coordinates.

int n_factor_ecm_select_curve(ulong *f, ulong sigma, ulong n, n_ecm_t n_ecm_inf)¶
Selects a random elliptic curve given a random integer
sigma
, according to Suyama’s parameterization. If the factor is found while selecting the curve, \(1\) is returned. In case the curve found is not suitable, \(0\) is returned.Also selects the initial point \(x_0\), and the value of \((a + 2)/4\), where \(a\) is a curve parameter. Sets \(z_0\) as \(1\) (shifted left by
n_ecm_inf>normbits
). All these are stored in then_ecm_t
struct.The curve selected is of Montgomery form, the points selected satisfy the curve and are projective coordinates.

int n_factor_ecm_stage_I(ulong *f, const ulong *prime_array, ulong num, ulong B1, ulong n, n_ecm_t n_ecm_inf)¶
Stage I implementation of the ECM algorithm.
f
is set as the factor if found.num
is number of prime numbers \(<=\) the boundB1
.prime_array
is an array of firstB1
primes. \(n\) is the number being factored.If the factor is found, \(1\) is returned, otherwise \(0\).

int n_factor_ecm_stage_II(ulong *f, ulong B1, ulong B2, ulong P, ulong n, n_ecm_t n_ecm_inf)¶
Stage II implementation of the ECM algorithm.
f
is set as the factor if found.B1
,B2
are the two bounds.P
is the primorial (approximately equal to \(\sqrt{B2}\)). \(n\) is the number being factored.If the factor is found, \(1\) is returned, otherwise \(0\).

int n_factor_ecm(ulong *f, ulong curves, ulong B1, ulong B2, flint_rand_t state, ulong n)¶
Outer wrapper function for the ECM algorithm. It factors \(n\) which must fit into a
ulong
.The function calls stage I and II, and the precomputations (builds
prime_array
for stage I,GCD_table
andprime_table
for stage II).f
is set as the factor if found.curves
is the number of random curves being tried.B1
,B2
are the two bounds or stage I and stage II. \(n\) is the number being factored.If a factor is found in stage I, \(1\) is returned. If a factor is found in stage II, \(2\) is returned. If a factor is found while selecting the curve, \(1\) is returned. Otherwise \(0\) is returned.