AndroidRemoteController/python/py/Lib/site-packages/sympy/polys/matrices/rref.py

# Algorithms for computing the reduced row echelon form of a matrix.
#
# We need to choose carefully which algorithms to use depending on the domain,
# shape, and sparsity of the matrix as well as things like the bit count in the
# case of ZZ or QQ. This is important because the algorithms have different
# performance characteristics in the extremes of dense vs sparse.
#
# In all cases we use the sparse implementations but we need to choose between
# Gauss-Jordan elimination with division and fraction-free Gauss-Jordan
# elimination. For very sparse matrices over ZZ with low bit counts it is
# asymptotically faster to use Gauss-Jordan elimination with division. For
# dense matrices with high bit counts it is asymptotically faster to use
# fraction-free Gauss-Jordan.
#
# The most important thing is to get the extreme cases right because it can
# make a big difference. In between the extremes though we have to make a
# choice and here we use empirically determined thresholds based on timings
# with random sparse matrices.
#
# In the case of QQ we have to consider the denominators as well. If the
# denominators are small then it is faster to clear them and use fraction-free
# Gauss-Jordan over ZZ. If the denominators are large then it is faster to use
# Gauss-Jordan elimination with division over QQ.
#
# Timings for the various algorithms can be found at
#
#   https://github.com/sympy/sympy/issues/25410
#   https://github.com/sympy/sympy/pull/25443

from sympy.polys.domains import ZZ

from sympy.polys.matrices.sdm import SDM, sdm_irref, sdm_rref_den
from sympy.polys.matrices.ddm import DDM
from sympy.polys.matrices.dense import ddm_irref, ddm_irref_den


def _dm_rref(M, *, method='auto'):
    """
    Compute the reduced row echelon form of a ``DomainMatrix``.

    This function is the implementation of :meth:`DomainMatrix.rref`.

    Chooses the best algorithm depending on the domain, shape, and sparsity of
    the matrix as well as things like the bit count in the case of :ref:`ZZ` or
    :ref:`QQ`. The result is returned over the field associated with the domain
    of the Matrix.

    See Also
    ========

    sympy.polys.matrices.domainmatrix.DomainMatrix.rref
        The ``DomainMatrix`` method that calls this function.
    sympy.polys.matrices.rref._dm_rref_den
        Alternative function for computing RREF with denominator.
    """
    method, use_fmt = _dm_rref_choose_method(M, method, denominator=False)

    M, old_fmt = _dm_to_fmt(M, use_fmt)

    if method == 'GJ':
        # Use Gauss-Jordan with division over the associated field.
        Mf = _to_field(M)
        M_rref, pivots = _dm_rref_GJ(Mf)

    elif method == 'FF':
        # Use fraction-free GJ over the current domain.
        M_rref_f, den, pivots = _dm_rref_den_FF(M)
        M_rref = _to_field(M_rref_f) / den

    elif method == 'CD':
        # Clear denominators and use fraction-free GJ in the associated ring.
        _, Mr = M.clear_denoms_rowwise(convert=True)
        M_rref_f, den, pivots = _dm_rref_den_FF(Mr)
        M_rref = _to_field(M_rref_f) / den

    else:
        raise ValueError(f"Unknown method for rref: {method}")

    M_rref, _ = _dm_to_fmt(M_rref, old_fmt)

    # Invariants:
    #   - M_rref is in the same format (sparse or dense) as the input matrix.
    #   - M_rref is in the associated field domain and any denominator was
    #     divided in (so is implicitly 1 now).

    return M_rref, pivots


def _dm_rref_den(M, *, keep_domain=True, method='auto'):
    """
    Compute the reduced row echelon form of a ``DomainMatrix`` with denominator.

    This function is the implementation of :meth:`DomainMatrix.rref_den`.

    Chooses the best algorithm depending on the domain, shape, and sparsity of
    the matrix as well as things like the bit count in the case of :ref:`ZZ` or
    :ref:`QQ`. The result is returned over the same domain as the input matrix
    unless ``keep_domain=False`` in which case the result might be over an
    associated ring or field domain.

    See Also
    ========

    sympy.polys.matrices.domainmatrix.DomainMatrix.rref_den
        The ``DomainMatrix`` method that calls this function.
    sympy.polys.matrices.rref._dm_rref
        Alternative function for computing RREF without denominator.
    """
    method, use_fmt = _dm_rref_choose_method(M, method, denominator=True)

    M, old_fmt = _dm_to_fmt(M, use_fmt)

    if method == 'FF':
        # Use fraction-free GJ over the current domain.
        M_rref, den, pivots = _dm_rref_den_FF(M)

    elif method == 'GJ':
        # Use Gauss-Jordan with division over the associated field.
        M_rref_f, pivots = _dm_rref_GJ(_to_field(M))

        # Convert back to the ring?
        if keep_domain and M_rref_f.domain != M.domain:
            _, M_rref = M_rref_f.clear_denoms(convert=True)

            if pivots:
                den = M_rref[0, pivots[0]].element
            else:
                den = M_rref.domain.one
        else:
            # Possibly an associated field
            M_rref = M_rref_f
            den = M_rref.domain.one

    elif method == 'CD':
        # Clear denominators and use fraction-free GJ in the associated ring.
        _, Mr = M.clear_denoms_rowwise(convert=True)

        M_rref_r, den, pivots = _dm_rref_den_FF(Mr)

        if keep_domain and M_rref_r.domain != M.domain:
            # Convert back to the field
            M_rref = _to_field(M_rref_r) / den
            den = M.domain.one
        else:
            # Possibly an associated ring
            M_rref = M_rref_r

            if pivots:
                den = M_rref[0, pivots[0]].element
            else:
                den = M_rref.domain.one
    else:
        raise ValueError(f"Unknown method for rref: {method}")

    M_rref, _ = _dm_to_fmt(M_rref, old_fmt)

    # Invariants:
    #   - M_rref is in the same format (sparse or dense) as the input matrix.
    #   - If keep_domain=True then M_rref and den are in the same domain as the
    #     input matrix
    #   - If keep_domain=False then M_rref might be in an associated ring or
    #     field domain but den is always in the same domain as M_rref.

    return M_rref, den, pivots


def _dm_to_fmt(M, fmt):
    """Convert a matrix to the given format and return the old format."""
    old_fmt = M.rep.fmt
    if old_fmt == fmt:
        pass
    elif fmt == 'dense':
        M = M.to_dense()
    elif fmt == 'sparse':
        M = M.to_sparse()
    else:
        raise ValueError(f'Unknown format: {fmt}') # pragma: no cover
    return M, old_fmt


# These are the four basic implementations that we want to choose between:


def _dm_rref_GJ(M):
    """Compute RREF using Gauss-Jordan elimination with division."""
    if M.rep.fmt == 'sparse':
        return _dm_rref_GJ_sparse(M)
    else:
        return _dm_rref_GJ_dense(M)


def _dm_rref_den_FF(M):
    """Compute RREF using fraction-free Gauss-Jordan elimination."""
    if M.rep.fmt == 'sparse':
        return _dm_rref_den_FF_sparse(M)
    else:
        return _dm_rref_den_FF_dense(M)


def _dm_rref_GJ_sparse(M):
    """Compute RREF using sparse Gauss-Jordan elimination with division."""
    M_rref_d, pivots, _ = sdm_irref(M.rep)
    M_rref_sdm = SDM(M_rref_d, M.shape, M.domain)
    pivots = tuple(pivots)
    return M.from_rep(M_rref_sdm), pivots


def _dm_rref_GJ_dense(M):
    """Compute RREF using dense Gauss-Jordan elimination with division."""
    partial_pivot = M.domain.is_RR or M.domain.is_CC
    ddm = M.rep.to_ddm().copy()
    pivots = ddm_irref(ddm, _partial_pivot=partial_pivot)
    M_rref_ddm = DDM(ddm, M.shape, M.domain)
    pivots = tuple(pivots)
    return M.from_rep(M_rref_ddm.to_dfm_or_ddm()), pivots


def _dm_rref_den_FF_sparse(M):
    """Compute RREF using sparse fraction-free Gauss-Jordan elimination."""
    M_rref_d, den, pivots = sdm_rref_den(M.rep, M.domain)
    M_rref_sdm = SDM(M_rref_d, M.shape, M.domain)
    pivots = tuple(pivots)
    return M.from_rep(M_rref_sdm), den, pivots


def _dm_rref_den_FF_dense(M):
    """Compute RREF using sparse fraction-free Gauss-Jordan elimination."""
    ddm = M.rep.to_ddm().copy()
    den, pivots = ddm_irref_den(ddm, M.domain)
    M_rref_ddm = DDM(ddm, M.shape, M.domain)
    pivots = tuple(pivots)
    return M.from_rep(M_rref_ddm.to_dfm_or_ddm()), den, pivots


def _dm_rref_choose_method(M, method, *, denominator=False):
    """Choose the fastest method for computing RREF for M."""

    if method != 'auto':
        if method.endswith('_dense'):
            method = method[:-len('_dense')]
            use_fmt = 'dense'
        else:
            use_fmt = 'sparse'

    else:
        # The sparse implementations are always faster
        use_fmt = 'sparse'

        K = M.domain

        if K.is_ZZ:
            method = _dm_rref_choose_method_ZZ(M, denominator=denominator)
        elif K.is_QQ:
            method = _dm_rref_choose_method_QQ(M, denominator=denominator)
        elif K.is_RR or K.is_CC:
            # TODO: Add partial pivot support to the sparse implementations.
            method = 'GJ'
            use_fmt = 'dense'
        elif K.is_EX and M.rep.fmt == 'dense' and not denominator:
            # Do not switch to the sparse implementation for EX because the
            # domain does not have proper canonicalization and the sparse
            # implementation gives equivalent but non-identical results over EX
            # from performing arithmetic in a different order. Specifically
            # test_issue_23718 ends up getting a more complicated expression
            # when using the sparse implementation. Probably the best fix for
            # this is something else but for now we stick with the dense
            # implementation for EX if the matrix is already dense.
            method = 'GJ'
            use_fmt = 'dense'
        else:
            # This is definitely suboptimal. More work is needed to determine
            # the best method for computing RREF over different domains.
            if denominator:
                method = 'FF'
            else:
                method = 'GJ'

    return method, use_fmt


def _dm_rref_choose_method_QQ(M, *, denominator=False):
    """Choose the fastest method for computing RREF over QQ."""
    # The same sorts of considerations apply here as in the case of ZZ. Here
    # though a new more significant consideration is what sort of denominators
    # we have and what to do with them so we focus on that.

    # First compute the density. This is the average number of non-zero entries
    # per row but only counting rows that have at least one non-zero entry
    # since RREF can ignore fully zero rows.
    density, _, ncols = _dm_row_density(M)

    # For sparse matrices use Gauss-Jordan elimination over QQ regardless.
    if density < min(5, ncols/2):
        return 'GJ'

    # Compare the bit-length of the lcm of the denominators to the bit length
    # of the numerators.
    #
    # The threshold here is empirical: we prefer rref over QQ if clearing
    # denominators would result in a numerator matrix having 5x the bit size of
    # the current numerators.
    numers, denoms = _dm_QQ_numers_denoms(M)
    numer_bits = max([n.bit_length() for n in numers], default=1)

    denom_lcm = ZZ.one
    for d in denoms:
        denom_lcm = ZZ.lcm(denom_lcm, d)
        if denom_lcm.bit_length() > 5*numer_bits:
            return 'GJ'

    # If we get here then the matrix is dense and the lcm of the denominators
    # is not too large compared to the numerators. For particularly small
    # denominators it is fastest just to clear them and use fraction-free
    # Gauss-Jordan over ZZ. With very small denominators this is a little
    # faster than using rref_den over QQ but there is an intermediate regime
    # where rref_den over QQ is significantly faster. The small denominator
    # case is probably very common because small fractions like 1/2 or 1/3 are
    # often seen in user inputs.

    if denom_lcm.bit_length() < 50:
        return 'CD'
    else:
        return 'FF'


def _dm_rref_choose_method_ZZ(M, *, denominator=False):
    """Choose the fastest method for computing RREF over ZZ."""
    # In the extreme of very sparse matrices and low bit counts it is faster to
    # use Gauss-Jordan elimination over QQ rather than fraction-free
    # Gauss-Jordan over ZZ. In the opposite extreme of dense matrices and high
    # bit counts it is faster to use fraction-free Gauss-Jordan over ZZ. These
    # two extreme cases need to be handled differently because they lead to
    # different asymptotic complexities. In between these two extremes we need
    # a threshold for deciding which method to use. This threshold is
    # determined empirically by timing the two methods with random matrices.

    # The disadvantage of using empirical timings is that future optimisations
    # might change the relative speeds so this can easily become out of date.
    # The main thing is to get the asymptotic complexity right for the extreme
    # cases though so the precise value of the threshold is hopefully not too
    # important.

    # Empirically determined parameter.
    PARAM = 10000

    # First compute the density. This is the average number of non-zero entries
    # per row but only counting rows that have at least one non-zero entry
    # since RREF can ignore fully zero rows.
    density, nrows_nz, ncols = _dm_row_density(M)

    # For small matrices use QQ if more than half the entries are zero.
    if nrows_nz < 10:
        if density < ncols/2:
            return 'GJ'
        else:
            return 'FF'

    # These are just shortcuts for the formula below.
    if density < 5:
        return 'GJ'
    elif density > 5 + PARAM/nrows_nz:
        return 'FF'  # pragma: no cover

    # Maximum bitsize of any entry.
    elements = _dm_elements(M)
    bits = max([e.bit_length() for e in elements], default=1)

    # Wideness parameter. This is 1 for square or tall matrices but >1 for wide
    # matrices.
    wideness = max(1, 2/3*ncols/nrows_nz)

    max_density = (5 + PARAM/(nrows_nz*bits**2)) * wideness

    if density < max_density:
        return 'GJ'
    else:
        return 'FF'


def _dm_row_density(M):
    """Density measure for sparse matrices.

    Defines the "density", ``d`` as the average number of non-zero entries per
    row except ignoring rows that are fully zero. RREF can ignore fully zero
    rows so they are excluded. By definition ``d >= 1`` except that we define
    ``d = 0`` for the zero matrix.

    Returns ``(density, nrows_nz, ncols)`` where ``nrows_nz`` counts the number
    of nonzero rows and ``ncols`` is the number of columns.
    """
    # Uses the SDM dict-of-dicts representation.
    ncols = M.shape[1]
    rows_nz = M.rep.to_sdm().values()
    if not rows_nz:
        return 0, 0, ncols
    else:
        nrows_nz = len(rows_nz)
        density = sum(map(len, rows_nz)) / nrows_nz
        return density, nrows_nz, ncols


def _dm_elements(M):
    """Return nonzero elements of a DomainMatrix."""
    elements, _ = M.to_flat_nz()
    return elements


def _dm_QQ_numers_denoms(Mq):
    """Returns the numerators and denominators of a DomainMatrix over QQ."""
    elements = _dm_elements(Mq)
    numers = [e.numerator for e in elements]
    denoms = [e.denominator for e in elements]
    return numers, denoms


def _to_field(M):
    """Convert a DomainMatrix to a field if possible."""
    K = M.domain
    if K.has_assoc_Field:
        return M.to_field()
    else:
        return M