.. _fmpz-lll:

**fmpz_lll.h** -- LLL reduction
==================================================================================================

Parameter manipulation
--------------------------------------------------------------------------------

These functions are used to initialise LLL context objects which are of the
type ``fmpz_lll_t``. These objects contain all information about the
options governing the reduction using this module's functions including the
LLL parameters \delta and \eta, the representation type of the input matrix
(whether it is a lattice basis or a Gram matrix), and the type of Gram
matrix to be used during L^2 (approximate or exact).

.. function:: void fmpz_lll_context_init_default(fmpz_lll_t fl)

    Sets ``fl->delta``, ``fl->eta``, ``fl->rt`` and ``fl->gt`` to
    their default values, 0.99, 0.51, `Z\_BASIS` and `APPROX` respectively.

.. function:: void fmpz_lll_context_init(fmpz_lll_t fl, double delta, double eta, rep_type rt, gram_type gt)

    Sets ``fl->delta``, ``fl->eta``, ``fl->rt`` and ``fl->gt`` to
    ``delta``, ``eta``, ``rt`` and ``gt`` (given as input)
    respectively. ``delta`` and ``eta`` are the L^2 parameters.
    ``delta`` and ``eta`` must lie in the intervals `(0.25, 1)` and
    `(0.5, \sqrt{\mathtt{delta}})` respectively. The representation type is input
    using ``rt`` and can have the values `Z\_BASIS` for a lattice basis and
    `GRAM` for a Gram matrix. The Gram type to be used during computation can
    be specified using ``gt`` which can assume the values `APPROX` and
    `EXACT`. Note that ``gt`` has meaning only when ``rt`` is `Z\_BASIS`.


Random parameter generation
--------------------------------------------------------------------------------


.. function:: void fmpz_lll_randtest(fmpz_lll_t fl, flint_rand_t state)

    Sets ``fl->delta`` and ``fl->eta`` to random values in the interval
    `(0.25, 1)` and `(0.5, \sqrt{\mathtt{delta}})` respectively. ``fl->rt`` is
    set to `GRAM` or `Z\_BASIS` and ``fl->gt`` is set to `APPROX` or `EXACT`
    in a pseudo random way.


Heuristic dot product
--------------------------------------------------------------------------------


.. function:: double fmpz_lll_heuristic_dot(const double * vec1, const double * vec2, slong len2, const fmpz_mat_t B, slong k, slong j, slong exp_adj)

    Computes the dot product of two vectors of doubles ``vec1`` and
    ``vec2``, which are respectively ``double`` approximations (up to
    scaling by a power of 2) to rows ``k`` and ``j`` in the exact integer
    matrix ``B``. If massive cancellation is detected an exact computation
    is made.

    The exact computation is scaled by `2^{-\mathtt{exp_adj}}`, where
    ``exp_adj = r2 + r1`` where `r2` is the exponent for row ``j`` and
    `r1` is the exponent for row ``k`` (i.e. row ``j`` is notionally
    thought of as being multiplied by `2^{r2}`, etc.).

    The final dot product computed by this function is then notionally the
    return value times `2^{\mathtt{exp_adj}}`.


The various Babai's
--------------------------------------------------------------------------------


.. function:: int fmpz_lll_check_babai(int kappa, fmpz_mat_t B, fmpz_mat_t U, d_mat_t mu, d_mat_t r, double *s, d_mat_t appB, int *expo, fmpz_gram_t A, int a, int zeros, int kappamax, int n, const fmpz_lll_t fl)

    Performs floating point size reductions of the ``kappa``-th row of
    ``B`` by all of the previous rows, uses d_mats ``mu`` and ``r``
    for storing the GSO data. ``U`` is used to capture the unimodular
    transformations if it is not `NULL`. The ``double`` array ``s`` will
    contain the size of the ``kappa``-th row if it were moved into position
    `i`. The d_mat ``appB`` is an approximation of ``B`` with each row
    receiving an exponent stored in ``expo`` which gets populated only when
    needed. The d_mat ``A->appSP`` is an approximation of the Gram matrix
    whose entries are scalar products of the rows of ``B`` and is used when
    ``fl->gt`` == `APPROX`. When ``fl->gt`` == `EXACT` the fmpz_mat
    ``A->exactSP`` (the exact Gram matrix) is used. The index ``a`` is
    the smallest row index which will be reduced from the ``kappa``-th row.
    Index ``zeros`` is the number of zero rows in the matrix.
    ``kappamax`` is the highest index which has been size-reduced so far,
    and ``n`` is the number of columns you want to consider. ``fl`` is an
    LLL (L^2) context object. The output is the value -1 if the process fails
    (usually due to insufficient precision) or 0 if everything was successful.
    These descriptions will be true for the future Babai procedures as well.

.. function:: int fmpz_lll_check_babai_heuristic_d(int kappa, fmpz_mat_t B, fmpz_mat_t U, d_mat_t mu, d_mat_t r, double *s, d_mat_t appB, int *expo, fmpz_gram_t A, int a, int zeros, int kappamax, int n, const fmpz_lll_t fl)

    Same as :func:`fmpz_lll_check_babai` but using the heuristic inner product
    rather than a purely floating point inner product. The heuristic will
    compute at full precision when there is cancellation.

.. function:: int fmpz_lll_check_babai_heuristic(int kappa, fmpz_mat_t B, fmpz_mat_t U, mpf_mat_t mu, mpf_mat_t r, mpf *s, mpf_mat_t appB, fmpz_gram_t A, int a, int zeros, int kappamax, int n, mpf_t tmp, mpf_t rtmp, flint_bitcnt_t prec, const fmpz_lll_t fl)

    This function is like the ``mpf`` version of
    :func:`fmpz_lll_check_babai_heuristic_d`. However, it also inherits some
    temporary ``mpf_t`` variables ``tmp`` and ``rtmp``.

.. function:: int fmpz_lll_advance_check_babai(int cur_kappa, int kappa, fmpz_mat_t B, fmpz_mat_t U, d_mat_t mu, d_mat_t r, double *s, d_mat_t appB, int *expo, fmpz_gram_t A, int a, int zeros, int kappamax, int n, const fmpz_lll_t fl)

    This is a Babai procedure which is used when size reducing a vector beyond
    an index which LLL has reached. ``cur_kappa`` is the index behind which
    we can assume ``B`` is LLL reduced, while ``kappa`` is the vector to
    be reduced. This procedure only size reduces the ``kappa``-th row by
    vectors up to ``cur_kappa``, **not** ``kappa - 1``.

.. function:: int fmpz_lll_advance_check_babai_heuristic_d(int cur_kappa, int kappa, fmpz_mat_t B, fmpz_mat_t U, d_mat_t mu, d_mat_t r, double *s, d_mat_t appB, int *expo, fmpz_gram_t A, int a, int zeros, int kappamax, int n, const fmpz_lll_t fl)

    Same as :func:`fmpz_lll_advance_check_babai` but using the heuristic inner
    product rather than a purely floating point inner product. The heuristic
    will compute at full precision when there is cancellation.


Shift
--------------------------------------------------------------------------------


.. function:: int fmpz_lll_shift(const fmpz_mat_t B)

    Computes the largest number of non-zero entries after the diagonal in
    ``B``.


Varieties of LLL
--------------------------------------------------------------------------------

These programs implement ideas from the book chapter [Stehle2010]_.
The list of function here that are heuristic in nature and may return with `B`
unreduced - that is to say, not do their job - includes (but is not necessarily limited to):

    * :func:`fmpz_lll_d`
    * :func:`fmpz_lll_d_heuristic`
    * :func:`fmpz_lll_d_heuristic_with_removal`
    * :func:`fmpz_lll_d_with_removal`
    * :func:`fmpz_lll_d_with_removal_knapsack`

.. function:: int fmpz_lll_d(fmpz_mat_t B, fmpz_mat_t U, const fmpz_lll_t fl)

    This is a mildly greedy version of floating point LLL using doubles only.
    It tries the fast version of the Babai algorithm
    (:func:`fmpz_lll_check_babai`). If that fails, then it switches to the
    heuristic version (:func:`fmpz_lll_check_babai_heuristic_d`) for only one
    loop and switches right back to the fast version. It reduces ``B`` in
    place. ``U`` is the matrix used to capture the unimodular
    transformations if it is not `NULL`. An exception is raised if `U` != `NULL`
    and ``U->r`` != `d`, where `d` is the lattice dimension. ``fl`` is the
    context object containing information containing the LLL parameters \delta
    and \eta. The function can perform reduction on both the lattice basis as
    well as its Gram matrix. The type of lattice representation can be
    specified via the parameter ``fl->rt``. The type of Gram matrix to be
    used in computation (approximate or exact) can also be specified through
    the variable ``fl->gt`` (applies only if ``fl->rt`` == `Z\_BASIS`).

.. function:: int fmpz_lll_d_heuristic(fmpz_mat_t B, fmpz_mat_t U, const fmpz_lll_t fl)

    This LLL reduces ``B`` in place using doubles only. It is similar to
    :func:`fmpz_lll_d` but only uses the heuristic inner products which
    attempt to detect cancellations.

.. function:: int fmpz_lll_mpf2(fmpz_mat_t B, fmpz_mat_t U, flint_bitcnt_t prec, const fmpz_lll_t fl)

    This is LLL using ``mpf`` with the given precision, ``prec`` for the
    underlying GSO. It reduces ``B`` in place like the other LLL functions.
    The `mpf2` in the function name refers to the way the ``mpf_t``'s are
    initialised.

.. function:: int fmpz_lll_mpf(fmpz_mat_t B, fmpz_mat_t U, const fmpz_lll_t fl)

    A wrapper of :func:`fmpz_lll_mpf2`. This currently begins with
    `prec == D\_BITS`, then for the first 20 loops, increases the precision one
    limb at a time. After 20 loops, it doubles the precision each time. There
    is a proof that this will eventually work. The return value of this
    function is 0 if the LLL is successful or -1 if the precision maxes out
    before ``B`` is LLL-reduced.

.. function:: int fmpz_lll_wrapper(fmpz_mat_t B, fmpz_mat_t U, const fmpz_lll_t fl)

    A wrapper of the above procedures. It begins with the greediest version
    (:func:`fmpz_lll_d`), then adapts to the version using heuristic inner
    products only (:func:`fmpz_lll_d_heuristic`) if ``fl->rt`` == `Z\_BASIS` and
    ``fl->gt`` == `APPROX`, and finally to the mpf version (:func:`fmpz_lll_mpf`)
    if needed.

    ``U`` is the matrix used to capture the unimodular
    transformations if it is not `NULL`. An exception is raised if `U` != `NULL`
    and ``U->r`` != `d`, where `d` is the lattice dimension. ``fl`` is the
    context object containing information containing the LLL parameters \delta
    and \eta. The function can perform reduction on both the lattice basis as
    well as its Gram matrix. The type of lattice representation can be
    specified via the parameter ``fl->rt``. The type of Gram matrix to be
    used in computation (approximate or exact) can also be specified through
    the variable ``fl->gt`` (applies only if ``fl->rt`` == `Z\_BASIS`).


.. function:: int fmpz_lll_d_with_removal(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    Same as :func:`fmpz_lll_d` but with a removal bound, ``gs_B``. The
    return value is the new dimension of ``B`` if removals are desired.

.. function:: int fmpz_lll_d_heuristic_with_removal(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    Same as :func:`fmpz_lll_d_heuristic` but with a removal bound,
    ``gs_B``. The return value is the new dimension of ``B`` if removals
    are desired.

.. function:: int fmpz_lll_mpf2_with_removal(fmpz_mat_t B, fmpz_mat_t U, flint_bitcnt_t prec, const fmpz_t gs_B, const fmpz_lll_t fl)

    Same as :func:`fmpz_lll_mpf2` but with a removal bound, ``gs_B``. The
    return value is the new dimension of ``B`` if removals are desired.

.. function:: int fmpz_lll_mpf_with_removal(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    A wrapper of :func:`fmpz_lll_mpf2_with_removal`. This currently begins
    with `prec == D\_BITS`, then for the first 20 loops, increases the precision
    one limb at a time. After 20 loops, it doubles the precision each time.
    There is a proof that this will eventually work. The return value of this
    function is the new dimension of ``B`` if removals are desired or -1 if
    the precision maxes out before ``B`` is LLL-reduced.

.. function:: int fmpz_lll_wrapper_with_removal(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    A wrapper of the procedures implementing the base case LLL with the
    addition of the removal boundary. It begins with the greediest version
    (:func:`fmpz_lll_d_with_removal`), then adapts to the version using
    heuristic inner products only (:func:`fmpz_lll_d_heuristic_with_removal`)
    if ``fl->rt`` == `Z\_BASIS` and ``fl->gt`` == `APPROX`, and finally to the mpf
    version (:func:`fmpz_lll_mpf_with_removal`) if needed.

.. function:: int fmpz_lll_d_with_removal_knapsack(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    This is floating point LLL specialized to knapsack-type lattices. It
    performs early size reductions occasionally which makes things faster in
    the knapsack case. Otherwise, it is similar to
    ``fmpz_lll_d_with_removal``.

.. function:: int fmpz_lll_wrapper_with_removal_knapsack(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    A wrapper of the procedures implementing the LLL specialized to
    knapsack-type lattices. It begins with the greediest version and the engine
    of this version, (:func:`fmpz_lll_d_with_removal_knapsack`), then adapts
    to the version using heuristic inner products only
    (:func:`fmpz_lll_d_heuristic_with_removal`) if ``fl->rt`` == `Z\_BASIS` and
    ``fl->gt`` == `APPROX`, and finally to the mpf version
    (:func:`fmpz_lll_mpf_with_removal`) if needed.


ULLL
--------------------------------------------------------------------------------


.. function:: int fmpz_lll_with_removal_ulll(fmpz_mat_t FM, fmpz_mat_t UM, slong new_size, const fmpz_t gs_B, const fmpz_lll_t fl)

    ULLL is a new style of LLL which adjoins an identity matrix to the
    input lattice ``FM``, then scales the lattice down to ``new_size``
    bits and reduces this augmented lattice. This tends to be more stable
    numerically than traditional LLL which means higher dimensions can be
    attacked using doubles. In each iteration a new identity matrix is adjoined
    to the truncated lattice. ``UM`` is used to capture the unimodular
    transformations, while ``gs_B`` and ``fl`` have the same role as in
    the previous routines. The function is optimised for factoring polynomials.


LLL-reducedness
--------------------------------------------------------------------------------

These programs implement ideas from the paper [Villard2007]_.
See https://arxiv.org/abs/cs/0701183 for the algorithm of Villard.

.. function:: int fmpz_lll_is_reduced_d(const fmpz_mat_t B, const fmpz_lll_t fl)
              int fmpz_lll_is_reduced_mpfr(const fmpz_mat_t B, const fmpz_lll_t fl, flint_bitcnt_t prec)
              int fmpz_lll_is_reduced_d_with_removal(const fmpz_mat_t B, const fmpz_lll_t fl, const fmpz_t gs_B, int newd)
              int fmpz_lll_is_reduced_mpfr_with_removal(const fmpz_mat_t B, const fmpz_lll_t fl, const fmpz_t gs_B, int newd, flint_bitcnt_t prec)

    A non-zero return indicates the matrix is definitely reduced, that is, that
    * :func:`fmpz_mat_is_reduced` or :func:`fmpz_mat_is_reduced_gram` (for the first two)
    * :func:`fmpz_mat_is_reduced_with_removal` or :func:`fmpz_mat_is_reduced_gram_with_removal` (for the last two)
    return non-zero. A zero return value is inconclusive.
    The `_d` variants are performed in machine precision, while the `_mpfr` uses a precision of `prec` bits.

.. function:: int fmpz_lll_is_reduced(const fmpz_mat_t B, const fmpz_lll_t fl, flint_bitcnt_t prec)
              int fmpz_lll_is_reduced_with_removal(const fmpz_mat_t B, const fmpz_lll_t fl, const fmpz_t gs_B, int newd, flint_bitcnt_t prec)

    The return from these functions is always conclusive: the functions
    * :func:`fmpz_mat_is_reduced` or :func:`fmpz_mat_is_reduced_gram`
    * :func:`fmpz_mat_is_reduced_with_removal` or :func:`fmpz_mat_is_reduced_gram_with_removal`
    are optimzied by calling the above heuristics first and returning right away if they give a conclusive answer.


Modified ULLL
--------------------------------------------------------------------------------


.. function:: void fmpz_lll_storjohann_ulll(fmpz_mat_t FM, slong new_size, const fmpz_lll_t fl)

    Performs ULLL using :func:`fmpz_mat_lll_storjohann` as the LLL function.


Main LLL functions
--------------------------------------------------------------------------------


.. function:: void fmpz_lll(fmpz_mat_t B, fmpz_mat_t U, const fmpz_lll_t fl)

    Reduces ``B`` in place according to the parameters specified by the
    LLL context object ``fl``.

    This is the main LLL function which should be called by the user. It
    currently calls the ULLL algorithm (without removals). The ULLL function
    in turn calls a LLL wrapper which tries to choose an optimal LLL algorithm,
    starting with a version using just doubles (ULLL tries to maximise usage
    of this), then a heuristic LLL followed by a full precision floating point
    LLL if required.

    ``U`` is the matrix used to capture the unimodular
    transformations if it is not `NULL`. An exception is raised if `U` != `NULL`
    and ``U->r`` != `d`, where `d` is the lattice dimension. ``fl`` is the
    context object containing information containing the LLL parameters \delta
    and \eta. The function can perform reduction on both the lattice basis as
    well as its Gram matrix. The type of lattice representation can be
    specified via the parameter ``fl->rt``. The type of Gram matrix to be
    used in computation (approximate or exact) can also be specified through
    the variable ``fl->gt`` (applies only if ``fl->rt`` == `Z\_BASIS`).


.. function:: int fmpz_lll_with_removal(fmpz_mat_t B, fmpz_mat_t U, const fmpz_t gs_B, const fmpz_lll_t fl)

    Reduces ``B`` in place according to the parameters specified by the
    LLL context object ``fl`` and removes vectors whose squared Gram-Schmidt
    length is greater than the bound ``gs_B``. The return value is the new
    dimension of ``B`` to be considered for further computation.

    This is the main LLL with removals function which should be called by
    the user. Like ``fmpz_lll`` it calls ULLL, but it also sets the
    Gram-Schmidt bound to that supplied and does removals.
