.. _arith:

**arith.h** -- arithmetic and special functions
================================================================================

This module implements arithmetic functions, number-theoretic and
combinatorial special number sequences and polynomials.

Harmonic numbers
--------------------------------------------------------------------------------


.. function:: void _arith_harmonic_number(fmpz_t num, fmpz_t den, slong n)
              void arith_harmonic_number(fmpq_t x, slong n)

    These are aliases for the functions in the fmpq module.


Stirling numbers
--------------------------------------------------------------------------------


.. function:: void arith_stirling_number_1u(fmpz_t s, ulong n, ulong k)

.. function:: void arith_stirling_number_1(fmpz_t s, ulong n, ulong k)

.. function:: void arith_stirling_number_2(fmpz_t s, ulong n, ulong k)

    Sets `s` to `S(n,k)` where `S(n,k)` denotes an unsigned Stirling
    number of the first kind `|S_1(n, k)|`, a signed Stirling number
    of the first kind `S_1(n, k)`, or a Stirling number of the second
    kind `S_2(n, k)`.  The Stirling numbers are defined using the
    generating functions

    .. math::

        x_{(n)} = \sum_{k=0}^n S_1(n,k) x^k

        x^{(n)} = \sum_{k=0}^n |S_1(n,k)| x^k

        x^n     = \sum_{k=0}^n S_2(n,k) x_{(k)}

    where `x_{(n)} = x(x-1)(x-2) \dotsm (x-n+1)` is a falling factorial
    and `x^{(n)} = x(x+1)(x+2) \dotsm (x+n-1)` is a rising factorial.
    `S(n,k)` is taken to be zero if `n < 0` or `k < 0`.

    These three functions are useful for computing isolated Stirling
    numbers efficiently. To compute a range of numbers, the vector or
    matrix versions should generally be used.

.. function:: void arith_stirling_number_1u_vec(fmpz * row, ulong n, slong klen)

.. function:: void arith_stirling_number_1_vec(fmpz * row, ulong n, slong klen)

.. function:: void arith_stirling_number_2_vec(fmpz * row, ulong n, slong klen)

    Computes the row of Stirling numbers
    ``S(n,0), S(n,1), S(n,2), ..., S(n,klen-1)``.

    To compute a full row, this function can be called with
    ``klen = n+1``. It is assumed that ``klen`` is at most `n + 1`.

.. function:: void arith_stirling_matrix_1u(fmpz_mat_t mat)

.. function:: void arith_stirling_matrix_1(fmpz_mat_t mat)

.. function:: void arith_stirling_matrix_2(fmpz_mat_t mat)

    For an arbitrary `m`-by-`n` matrix, writes the truncation of the
    infinite Stirling number matrix::

        row 0   : S(0,0)
        row 1   : S(1,0), S(1,1)
        row 2   : S(2,0), S(2,1), S(2,2)
        row 3   : S(3,0), S(3,1), S(3,2), S(3,3)

    up to row `m-1` and column `n-1` inclusive. The upper triangular
    part of the matrix is zeroed.

    For any `n`, the `S_1` and `S_2` matrices thus obtained are
    inverses of each other.


Bell numbers
--------------------------------------------------------------------------------


.. function:: void arith_bell_number(fmpz_t b, ulong n)
              void arith_bell_number_dobinski(fmpz_t res, ulong n)
              void arith_bell_number_multi_mod(fmpz_t res, ulong n)

    Sets `b` to the Bell number `B_n`, defined as the
    number of partitions of a set with `n` members. Equivalently,
    `B_n = \sum_{k=0}^n S_2(n,k)` where `S_2(n,k)` denotes a Stirling number
    of the second kind.

    The default version automatically selects between table lookup,
    Dobinski's formula, and the multimodular algorithm.

    The ``dobinski`` version evaluates a precise truncation of
    the series `B_n = e^{-1} \sum_{k=0}^{\infty} \frac{k^n}{k!}`
    (Dobinski's formula). In fact, we compute `P = N! \sum_{k=0}^N \frac{k^n}{k!}`
    and `Q = N! \sum_{k=0}^N \frac{1}{k!} \approx N! e` and
    evaluate `B_n = \lceil P / Q \rceil`, avoiding the use
    of floating-point arithmetic.

    The ``multi_mod`` version computes the result modulo several limb-size
    primes and reconstructs the integer value using the fast
    Chinese remainder algorithm.
    A bound for the number of needed primes is computed using
    ``arith_bell_number_size``.

.. function:: void arith_bell_number_vec(fmpz * b, slong n)
              void arith_bell_number_vec_recursive(fmpz * b, slong n)
              void arith_bell_number_vec_multi_mod(fmpz * b, slong n)

    Sets `b` to the vector of Bell numbers `B_0, B_1, \ldots, B_{n-1}`
    inclusive. The ``recursive`` version uses the `O(n^3 \log n)`
    triangular recurrence, while the ``multi_mod`` version implements
    multimodular evaluation of the exponential generating function,
    running in time `O(n^2 \log^{O(1)} n)`. The default version
    chooses an algorithm automatically.

.. function:: ulong arith_bell_number_nmod(ulong n, nmod_t mod)

    Computes the Bell number `B_n` modulo an integer given by ``mod``.

    After handling special cases, we use the formula

    .. math::

        B_n = \sum_{k=0}^n \frac{(n-k)^n}{(n-k)!}
            \sum_{j=0}^k \frac{(-1)^j}{j!}.

    We arrange the operations in such a way that we only have to
    multiply (and not divide) in the main loop. As a further optimisation,
    we use sieving to reduce the number of powers that need to be
    evaluated. This results in `O(n)` memory usage.

    If the divisions by factorials are impossible, we fall back to
    calling ``arith_bell_number_nmod_vec`` and reading the last
    coefficient.

.. function:: void arith_bell_number_nmod_vec(nn_ptr b, slong n, nmod_t mod)
              void arith_bell_number_nmod_vec_recursive(nn_ptr b, slong n, nmod_t mod)
              void arith_bell_number_nmod_vec_ogf(nn_ptr b, slong n, nmod_t mod)
              int arith_bell_number_nmod_vec_series(nn_ptr b, slong n, nmod_t mod)

    Sets `b` to the vector of Bell numbers `B_0, B_1, \ldots, B_{n-1}`
    inclusive modulo an integer given by ``mod``.

    The *recursive* version uses the `O(n^2)` triangular recurrence.
    The *ogf* version expands the ordinary generating function
    using binary splitting, which is `O(n \log^2 n)`.

    The *series* version uses the exponential generating function
    `\sum_{k=0}^{\infty} \frac{B_n}{n!} x^n = \exp(e^x-1)`,
    running in `O(n \log n)`.
    This only works if division by `n!` is possible, and the function
    returns whether it is successful. All other versions
    support any modulus.

    The default version of this function selects an algorithm
    automatically.

.. function:: double arith_bell_number_size(ulong n)

    Returns `b` such that `B_n < 2^{\lfloor b \rfloor}`. A previous
    version of this function used the inequality
    `B_n < \left(\frac{0.792n}{\log(n+1)}\right)^n` which is given
    in [BerTas2010]_; we now use a slightly better bound
    based on an asymptotic expansion.

Bernoulli numbers and polynomials
--------------------------------------------------------------------------------


.. function:: void _arith_bernoulli_number(fmpz_t num, fmpz_t den, ulong n)

    Sets ``(num, den)`` to the reduced numerator and denominator
    of the `n`-th Bernoulli number.

.. function:: void arith_bernoulli_number(fmpq_t x, ulong n)

    Sets ``x`` to the `n`-th Bernoulli number. This function is
    equivalent to ``_arith_bernoulli_number`` apart from the output
    being a single ``fmpq_t`` variable.

.. function:: void _arith_bernoulli_number_vec(fmpz * num, fmpz * den, slong n)

    Sets the elements of ``num`` and ``den`` to the reduced
    numerators and denominators of the Bernoulli numbers
    `B_0, B_1, B_2, \ldots, B_{n-1}` inclusive. This function automatically
    chooses between the ``recursive``, ``zeta`` and ``multi_mod``
    algorithms according to the size of `n`.

.. function:: void arith_bernoulli_number_vec(fmpq * x, slong n)

    Sets the ``x`` to the vector of Bernoulli numbers
    `B_0, B_1, B_2, \ldots, B_{n-1}` inclusive. This function is
    equivalent to ``_arith_bernoulli_number_vec`` apart
    from the output being a single ``fmpq`` vector.

.. function:: void arith_bernoulli_number_denom(fmpz_t den, ulong n)

    Sets ``den`` to the reduced denominator of the `n`-th
    Bernoulli number `B_n`. For even `n`, the denominator is computed
    as the product of all primes `p` for which `p - 1` divides `n`;
    this property is a consequence of the von Staudt-Clausen theorem.
    For odd `n`, the denominator is trivial (``den`` is set to 1 whenever
    `B_n = 0`). The initial sequence of values smaller than `2^{32}` are
    looked up directly from a table.

.. function:: double arith_bernoulli_number_size(ulong n)

    Returns `b` such that `|B_n| < 2^{\lfloor b \rfloor}`, using the inequality
    `|B_n| < \frac{4 n!}{(2\pi)^n}` and `n! \le (n+1)^{n+1} e^{-n}`.
    No special treatment is given to odd `n`. Accuracy is not guaranteed
    if `n > 10^{14}`.

.. function:: void arith_bernoulli_polynomial(fmpq_poly_t poly, ulong n)

    Sets ``poly`` to the Bernoulli polynomial of degree `n`,
    `B_n(x) = \sum_{k=0}^n \binom{n}{k} B_k x^{n-k}` where `B_k`
    is a Bernoulli number. This function basically calls
    ``arith_bernoulli_number_vec`` and then rescales the coefficients
    efficiently.

.. function:: void _arith_bernoulli_number_vec_recursive(fmpz * num, fmpz * den, slong n)

    Sets the elements of ``num`` and ``den`` to the reduced
    numerators and denominators of `B_0, B_1, B_2, \ldots, B_{n-1}`
    inclusive.

    The first few entries are computed using ``arith_bernoulli_number``,
    and then Ramanujan's recursive formula expressing `B_m` as a sum over
    `B_k` for `k` congruent to `m` modulo 6 is applied repeatedly.

    To avoid costly GCDs, the numerators are transformed internally
    to a common denominator and all operations are performed using
    integer arithmetic. This makes the algorithm fast for small `n`,
    say `n < 1000`. The common denominator is calculated directly
    as the primorial of `n + 1`.

    %[1] https://en.wikipedia.org/w/index.php?title=Bernoulli_number&oldid=405938876

.. function:: void _arith_bernoulli_number_vec_multi_mod(fmpz * num, fmpz * den, slong n)

    Sets the elements of ``num`` and ``den`` to the reduced
    numerators and denominators of `B_0, B_1, B_2, \ldots, B_{n-1}`
    inclusive. Uses the generating function

    .. math::

        \frac{x^2}{\cosh(x)-1} = \sum_{k=0}^{\infty}
            \frac{(2-4k) B_{2k}}{(2k)!} x^{2k}

    which is evaluated modulo several limb-size primes using ``nmod_poly``
    arithmetic to yield the numerators of the Bernoulli numbers after
    multiplication by the denominators and CRT reconstruction. This formula,
    given (incorrectly) in [BuhlerCrandallSompolski1992]_, saves about
    half of the time compared to the usual generating function `x/(e^x-1)`
    since the odd terms vanish.


Euler numbers and polynomials
--------------------------------------------------------------------------------

Euler numbers are the integers `E_n` defined by
`\frac{1}{\cosh(t)} = \sum_{n=0}^{\infty} \frac{E_n}{n!} t^n.`
With this convention, the odd-indexed numbers are zero and the even
ones alternate signs, viz.
`E_0, E_1, E_2, \ldots = 1, 0, -1, 0, 5, 0, -61, 0, 1385, 0, \ldots`.
The corresponding Euler polynomials are defined by
`\frac{2e^{xt}}{e^t+1} = \sum_{n=0}^{\infty} \frac{E_n(x)}{n!} t^n.`

.. function:: void arith_euler_number(fmpz_t res, ulong n)

    Sets ``res`` to the Euler number `E_n`.

.. function:: void arith_euler_number_vec(fmpz * res, slong n)

    Computes the Euler numbers `E_0, E_1, \dotsc, E_{n-1}` for `n \geq 0`
    and stores the result in ``res``, which must be an initialised
    ``fmpz`` vector of sufficient size.

    This function evaluates the even-index `E_k` modulo several limb-size
    primes using the generating function and ``nmod_poly`` arithmetic.
    A tight bound for the number of needed primes is computed using
    ``arith_euler_number_size``, and the final integer values are recovered
    using balanced CRT reconstruction.

.. function:: double arith_euler_number_size(ulong n)

    Returns `b` such that `|E_n| < 2^{\lfloor b \rfloor}`, using the inequality
    ``|E_n| < \frac{2^{n+2} n!}{\pi^{n+1}}`` and `n! \le (n+1)^{n+1} e^{-n}`.
    No special treatment is given to odd `n`.
    Accuracy is not guaranteed if `n > 10^{14}`.

.. function:: void arith_euler_polynomial(fmpq_poly_t poly, ulong n)

    Sets ``poly`` to the Euler polynomial `E_n(x)`. Uses the formula

    .. math::

        E_n(x) = \frac{2}{n+1}\left(B_{n+1}(x) -
            2^{n+1}B_{n+1}\left(\frac{x}{2}\right)\right),

    with the Bernoulli polynomial `B_{n+1}(x)` evaluated once
    using ``bernoulli_polynomial`` and then rescaled.


Multiplicative functions
--------------------------------------------------------------------------------


.. function:: void arith_divisors(fmpz_poly_t res, const fmpz_t n)

    Set the coefficients of the polynomial ``res`` to the divisors of `n`,
    including `1` and `n` itself, in ascending order.

.. function:: void arith_ramanujan_tau(fmpz_t res, const fmpz_t n)

    Sets ``res`` to the Ramanujan tau function `\tau(n)` which is the
    coefficient of `q^n` in the series expansion of
    `f(q) = q  \prod_{k \geq 1} \bigl(1 - q^k\bigr)^{24}`.

    We factor `n` and use the identity `\tau(pq) = \tau(p) \tau(q)`
    along with the recursion
    `\tau(p^{r+1}) = \tau(p) \tau(p^r) - p^{11} \tau(p^{r-1})`
    for prime powers.

    The base values `\tau(p)` are obtained using the function
    ``arith_ramanujan_tau_series()``. Thus the speed of
    ``arith_ramanujan_tau()`` depends on the largest prime factor of `n`.

    Future improvement:  optimise this function for small `n`, which
    could be accomplished using a lookup table or by calling
    ``arith_ramanujan_tau_series()`` directly.

.. function:: void arith_ramanujan_tau_series(fmpz_poly_t res, slong n)

    Sets ``res`` to the polynomial with coefficients
    `\tau(0),\tau(1), \dotsc, \tau(n-1)`, giving the initial `n` terms
    in the series expansion of
    `f(q) = q \prod_{k \geq 1} \bigl(1-q^k\bigr)^{24}`.

    We use the theta function identity


    .. math::


        f(q) = q  \Biggl( \sum_{k \geq 0} (-1)^k (2k+1) q^{k(k+1)/2} \Biggr)^8



    which is evaluated using three squarings. The first squaring is done
    directly since the polynomial is very sparse at this point.


Landau's function
--------------------------------------------------------------------------------


.. function:: void arith_landau_function_vec(fmpz * res, slong len)

    Computes the first ``len`` values of Landau's function `g(n)`
    starting with `g(0)`. Landau's function gives the largest order
    of an element of the symmetric group `S_n`.

    Implements the "basic algorithm" given in
    [DelegliseNicolasZimmermann2009]_. The running time is
    `O(n^{3/2} / \sqrt{\log n})`.


Number of partitions
--------------------------------------------------------------------------------


.. function:: void arith_number_of_partitions_vec(fmpz * res, slong len)

    Computes first ``len`` values of the partition function `p(n)`
    starting with `p(0)`. Uses inversion of Euler's pentagonal series.

.. function:: void arith_number_of_partitions_nmod_vec(nn_ptr res, slong len, nmod_t mod)

    Computes first ``len`` values of the partition function `p(n)`
    starting with `p(0)`, modulo the modulus defined by ``mod``.
    Uses inversion of Euler's pentagonal series.

.. function:: void trig_prod_init(trig_prod_t prod)

    Initializes ``prod``. This is an inline function only.

.. function:: void arith_hrr_expsum_factored(trig_prod_t prod, ulong k, ulong n)

    Symbolically evaluates the exponential sum

    .. math::

        A_k(n) = \sum_{h=0}^{k-1}
            \exp\left(\pi i \left[ s(h,k) - \frac{2hn}{k}\right]\right)

    appearing in the Hardy-Ramanujan-Rademacher formula, where `s(h,k)` is a
    Dedekind sum.

    Rather than evaluating the sum naively, we factor `A_k(n)` into a
    product of cosines based on the prime factorisation of `k`. This
    process is based on the identities given in [Whiteman1956]_.

    The special ``trig_prod_t`` structure ``prod`` represents a
    product of cosines of rational arguments, multiplied by an algebraic
    prefactor. It must be pre-initialised with ``trig_prod_init``.

    This function assumes that `24k` and `24n` do not overflow a single limb.
    If `n` is larger, it can be pre-reduced modulo `k`, since `A_k(n)`
    only depends on the value of `n \bmod k`.

.. function:: void arith_number_of_partitions_mpfr(mpfr_t x, ulong n)

    Sets the pre-initialised MPFR variable `x` to the exact value of `p(n)`.
    The value is computed using the Hardy-Ramanujan-Rademacher formula.

    The precision of `x` will be changed to allow `p(n)` to be represented
    exactly. The interface of this function may be updated in the future
    to allow computing an approximation of `p(n)` to smaller precision.

    The Hardy-Ramanujan-Rademacher formula is given with error bounds
    in [Rademacher1937]_. We evaluate it in the form

    .. math::

        p(n) = \sum_{k=1}^N B_k(n) U(C/k) + R(n,N)

    where

    .. math::

        U(x) = \cosh(x) + \frac{\sinh(x)}{x},
            \quad C = \frac{\pi}{6} \sqrt{24n-1}

        B_k(n) = \sqrt{\frac{3}{k}} \frac{4}{24n-1} A_k(n)

    and where `A_k(n)` is a certain exponential sum. The remainder satisfies

    .. math::

        |R(n,N)| < \frac{44 \pi^2}{225 \sqrt{3}} N^{-1/2} +
            \frac{\pi \sqrt{2}}{75} \left(\frac{N}{n-1}\right)^{1/2}
            \sinh\left(\pi \sqrt{\frac{2}{3}} \frac{\sqrt{n}}{N} \right).

    We choose `N` such that `|R(n,N)| < 0.25`, and a working precision
    at term `k` such that the absolute error of the term is expected to be
    less than `0.25 / N`. We also use a summation variable with increased
    precision, essentially making additions exact. Thus the sum of errors
    adds up to less than 0.5, giving the correct value of `p(n)` when
    rounding to the nearest integer.

    The remainder estimate at step `k` provides an upper bound for the size
    of the `k`-th term. We add `\log_2 N` bits to get low bits in the terms
    below `0.25 / N` in magnitude.

    Using ``arith_hrr_expsum_factored``, each `B_k(n)` evaluation
    is broken down to a product of cosines of exact rational multiples
    of `\pi`. We transform all angles to `(0, \pi/4)` for optimal accuracy.

    Since the evaluation of each term involves only `O(\log k)` multiplications
    and evaluations of trigonometric functions of small angles, the
    relative rounding error is at most a few bits. We therefore just add
    an additional `\log_2 (C/k)` bits for the `U(x)` when `x` is large.
    The cancellation of terms in `U(x)` is of no concern, since Rademacher's
    bound allows us to terminate before `x` becomes small.

    This analysis should be performed in more detail to give a rigorous
    error bound, but the precision currently implemented is almost
    certainly sufficient, not least considering that Rademacher's
    remainder bound significantly overshoots the actual values.

    To improve performance, we switch to doubles when the working precision
    becomes small enough. We also use a separate accumulator variable
    which gets added to the main sum periodically, in order to avoid
    costly updates of the full-precision result when `n` is large.

.. function:: void arith_number_of_partitions(fmpz_t x, ulong n)

    Sets `x` to `p(n)`, the number of ways that `n` can be written
    as a sum of positive integers without regard to order.

    This function uses a lookup table for `n < 128` (where `p(n) < 2^{32}`),
    and otherwise calls ``arith_number_of_partitions_mpfr``.


Sums of squares
--------------------------------------------------------------------------------


.. function:: void arith_sum_of_squares(fmpz_t r, ulong k, const fmpz_t n)

    Sets `r` to the number of ways `r_k(n)` in which `n` can be represented
    as a sum of `k` squares.

    If `k = 2` or `k = 4`, we write `r_k(n)` as a divisor sum.

    Otherwise, we either recurse on `k` or compute the theta function
    expansion up to `O(x^{n+1})` and read off the last coefficient.
    This is generally optimal.

.. function:: void arith_sum_of_squares_vec(fmpz * r, ulong k, slong n)

    For `i = 0, 1, \ldots, n-1`, sets `r_i` to the number of
    representations of `i` a sum of `k` squares, `r_k(i)`.
    This effectively computes the `q`-expansion of `\vartheta_3(q)`
    raised to the `k`-th power, i.e.

    .. math::

        \vartheta_3^k(q) = \left( \sum_{i=-\infty}^{\infty} q^{i^2} \right)^k.
