Dense matrices over \(\ZZ/n\ZZ\) for \(n < 2^{8}\) using LinBox’s Modular<float>#

AUTHORS: - Burcin Erocal - Martin Albrecht

class sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_float[source]#

Bases: Matrix_modn_dense_template

Dense matrices over \(\ZZ/n\ZZ\) for \(n < 2^{8}\) using LinBox’s Modular<float>

These are matrices with integer entries mod n represented as floating-point numbers in a 32-bit word for use with LinBox routines. This could allow for n up to \(2^{11}\), but for performance reasons this is limited to n up to \(2^{8}\), and for larger moduli the Matrix_modn_dense_double class is used.

Routines here are for the most basic access, see the matrix_modn_dense_template.pxi file for higher-level routines.

class sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_template[source]#

Bases: Matrix_dense

Create a new matrix.

INPUT:

  • parent – a matrix space

  • entries – see matrix()

  • copy – ignored (for backwards compatibility)

  • coerce – perform modular reduction first?

EXAMPLES:

sage: A = random_matrix(GF(3),1000,1000)
sage: type(A)
<class 'sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_float'>
sage: A = random_matrix(Integers(10),1000,1000)
sage: type(A)
<class 'sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_float'>
sage: A = random_matrix(Integers(2^16),1000,1000)
sage: type(A)
<class 'sage.matrix.matrix_modn_dense_double.Matrix_modn_dense_double'>

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(3)),Integer(1000),Integer(1000))
>>> type(A)
<class 'sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_float'>
>>> A = random_matrix(Integers(Integer(10)),Integer(1000),Integer(1000))
>>> type(A)
<class 'sage.matrix.matrix_modn_dense_float.Matrix_modn_dense_float'>
>>> A = random_matrix(Integers(Integer(2)**Integer(16)),Integer(1000),Integer(1000))
>>> type(A)
<class 'sage.matrix.matrix_modn_dense_double.Matrix_modn_dense_double'>
charpoly(var='x', algorithm='linbox')[source]#

Return the characteristic polynomial of self.

INPUT:

  • var – a variable name

  • algorithm – ‘generic’, ‘linbox’ or ‘all’ (default: linbox)

EXAMPLES:

sage: A = random_matrix(GF(19), 10, 10)
sage: B = copy(A)
sage: char_p = A.characteristic_polynomial()
sage: char_p(A) == 0
True
sage: B == A              # A is not modified
True

sage: min_p = A.minimal_polynomial(proof=True)
sage: min_p.divides(char_p)
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(19)), Integer(10), Integer(10))
>>> B = copy(A)
>>> char_p = A.characteristic_polynomial()
>>> char_p(A) == Integer(0)
True
>>> B == A              # A is not modified
True

>>> min_p = A.minimal_polynomial(proof=True)
>>> min_p.divides(char_p)
True

sage: A = random_matrix(GF(2916337), 7, 7)                                  # needs sage.rings.finite_rings
sage: B = copy(A)
sage: char_p = A.characteristic_polynomial()
sage: char_p(A) == 0
True
sage: B == A               # A is not modified
True

sage: min_p = A.minimal_polynomial(proof=True)
sage: min_p.divides(char_p)
True

sage: A = Mat(Integers(6),3,3)(range(9))
sage: A.charpoly()
x^3

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(2916337)), Integer(7), Integer(7))                                  # needs sage.rings.finite_rings
>>> B = copy(A)
>>> char_p = A.characteristic_polynomial()
>>> char_p(A) == Integer(0)
True
>>> B == A               # A is not modified
True

>>> min_p = A.minimal_polynomial(proof=True)
>>> min_p.divides(char_p)
True

>>> A = Mat(Integers(Integer(6)),Integer(3),Integer(3))(range(Integer(9)))
>>> A.charpoly()
x^3

ALGORITHM: Uses LinBox if self.base_ring() is a field, otherwise use Hessenberg form algorithm.

determinant()[source]#

Return the determinant of this matrix.

EXAMPLES:

sage: s = set()
sage: while s != set(GF(7)):
....:     A = random_matrix(GF(7), 10, 10)
....:     s.add(A.determinant())

>>> from sage.all import *
>>> s = set()
>>> while s != set(GF(Integer(7))):
...     A = random_matrix(GF(Integer(7)), Integer(10), Integer(10))
...     s.add(A.determinant())

sage: A = random_matrix(GF(7), 100, 100)
sage: A.determinant() == A.transpose().determinant()
True

sage: B = random_matrix(GF(7), 100, 100)
sage: (A*B).determinant() == A.determinant() * B.determinant()
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(7)), Integer(100), Integer(100))
>>> A.determinant() == A.transpose().determinant()
True

>>> B = random_matrix(GF(Integer(7)), Integer(100), Integer(100))
>>> (A*B).determinant() == A.determinant() * B.determinant()
True

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(16007), 10, 10)
sage: A.determinant().parent() is GF(16007)
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(16007)), Integer(10), Integer(10))
>>> A.determinant().parent() is GF(Integer(16007))
True

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(16007), 100, 100)
sage: A.determinant().parent() is GF(16007)
True
sage: A.determinant() == A.transpose().determinant()
True
sage: B = random_matrix(GF(16007), 100, 100)
sage: (A*B).determinant() == A.determinant() * B.determinant()
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(16007)), Integer(100), Integer(100))
>>> A.determinant().parent() is GF(Integer(16007))
True
>>> A.determinant() == A.transpose().determinant()
True
>>> B = random_matrix(GF(Integer(16007)), Integer(100), Integer(100))
>>> (A*B).determinant() == A.determinant() * B.determinant()
True

Parallel computation:

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(65521),200)
sage: B = copy(A)
sage: Parallelism().set('linbox', nproc=2)
sage: d = A.determinant()
sage: Parallelism().set('linbox', nproc=1) # switch off parallelization
sage: e = B.determinant()
sage: d==e
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(65521)),Integer(200))
>>> B = copy(A)
>>> Parallelism().set('linbox', nproc=Integer(2))
>>> d = A.determinant()
>>> Parallelism().set('linbox', nproc=Integer(1)) # switch off parallelization
>>> e = B.determinant()
>>> d==e
True
echelonize(algorithm='linbox_noefd', **kwds)[source]#

Put self in reduced row echelon form.

INPUT:

  • self – a mutable matrix

  • algorithm

    • linbox – uses the LinBox library (wrapping fflas-ffpack)

    • linbox_noefd – uses the FFPACK directly, less memory and faster (default)

    • gauss – uses a custom slower \(O(n^3)\) Gauss elimination implemented in Sage.

    • all – compute using both algorithms and verify that the results are the same.

  • **kwds – these are all ignored

OUTPUT:

  • self is put in reduced row echelon form.

  • the rank of self is computed and cached

  • the pivot columns of self are computed and cached.

  • the fact that self is now in echelon form is recorded and cached so future calls to echelonize return immediately.

EXAMPLES:

sage: A = random_matrix(GF(7), 10, 20)
sage: E = A.echelon_form()
sage: A.row_space() == E.row_space()
True
sage: all(r[r.nonzero_positions()[0]] == 1 for r in E.rows() if r)
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(7)), Integer(10), Integer(20))
>>> E = A.echelon_form()
>>> A.row_space() == E.row_space()
True
>>> all(r[r.nonzero_positions()[Integer(0)]] == Integer(1) for r in E.rows() if r)
True

sage: A = random_matrix(GF(13), 10, 10)
sage: while A.rank() != 10:
....:     A = random_matrix(GF(13), 10, 10)
sage: MS = parent(A)
sage: B = A.augment(MS(1))
sage: B.echelonize()
sage: A.rank()
10
sage: C = B.submatrix(0,10,10,10)
sage: ~A == C
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(13)), Integer(10), Integer(10))
>>> while A.rank() != Integer(10):
...     A = random_matrix(GF(Integer(13)), Integer(10), Integer(10))
>>> MS = parent(A)
>>> B = A.augment(MS(Integer(1)))
>>> B.echelonize()
>>> A.rank()
10
>>> C = B.submatrix(Integer(0),Integer(10),Integer(10),Integer(10))
>>> ~A == C
True

sage: A = random_matrix(Integers(10), 10, 20)
sage: A.echelon_form()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 10'.

>>> from sage.all import *
>>> A = random_matrix(Integers(Integer(10)), Integer(10), Integer(20))
>>> A.echelon_form()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 10'.

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(16007), 10, 20)
sage: E = A.echelon_form()
sage: A.row_space() == E.row_space()
True
sage: all(r[r.nonzero_positions()[0]] == 1 for r in E.rows() if r)
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(16007)), Integer(10), Integer(20))
>>> E = A.echelon_form()
>>> A.row_space() == E.row_space()
True
>>> all(r[r.nonzero_positions()[Integer(0)]] == Integer(1) for r in E.rows() if r)
True

sage: A = random_matrix(Integers(10000), 10, 20)
sage: A.echelon_form()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 10000'.

>>> from sage.all import *
>>> A = random_matrix(Integers(Integer(10000)), Integer(10), Integer(20))
>>> A.echelon_form()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 10000'.

Parallel computation:

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(65521),100,200)
sage: Parallelism().set('linbox', nproc=2)
sage: E = A.echelon_form()
sage: Parallelism().set('linbox', nproc=1) # switch off parallelization
sage: F = A.echelon_form()
sage: E==F
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(65521)),Integer(100),Integer(200))
>>> Parallelism().set('linbox', nproc=Integer(2))
>>> E = A.echelon_form()
>>> Parallelism().set('linbox', nproc=Integer(1)) # switch off parallelization
>>> F = A.echelon_form()
>>> E==F
True
hessenbergize()[source]#

Transforms self in place to its Hessenberg form.

EXAMPLES:

sage: A = random_matrix(GF(17), 10, 10, density=0.1)
sage: B = copy(A)
sage: A.hessenbergize()
sage: all(A[i,j] == 0 for j in range(10) for i in range(j+2, 10))
True
sage: A.charpoly() == B.charpoly()
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(17)), Integer(10), Integer(10), density=RealNumber('0.1'))
>>> B = copy(A)
>>> A.hessenbergize()
>>> all(A[i,j] == Integer(0) for j in range(Integer(10)) for i in range(j+Integer(2), Integer(10)))
True
>>> A.charpoly() == B.charpoly()
True
lift()[source]#

Return the lift of this matrix to the integers.

EXAMPLES:

sage: A = matrix(GF(7),2,3,[1..6])
sage: A.lift()
[1 2 3]
[4 5 6]
sage: A.lift().parent()
Full MatrixSpace of 2 by 3 dense matrices over Integer Ring

sage: # needs sage.rings.finite_rings
sage: A = matrix(GF(16007),2,3,[1..6])
sage: A.lift()
[1 2 3]
[4 5 6]
sage: A.lift().parent()
Full MatrixSpace of 2 by 3 dense matrices over Integer Ring

>>> from sage.all import *
>>> A = matrix(GF(Integer(7)),Integer(2),Integer(3),(ellipsis_range(Integer(1),Ellipsis,Integer(6))))
>>> A.lift()
[1 2 3]
[4 5 6]
>>> A.lift().parent()
Full MatrixSpace of 2 by 3 dense matrices over Integer Ring

>>> # needs sage.rings.finite_rings
>>> A = matrix(GF(Integer(16007)),Integer(2),Integer(3),(ellipsis_range(Integer(1),Ellipsis,Integer(6))))
>>> A.lift()
[1 2 3]
[4 5 6]
>>> A.lift().parent()
Full MatrixSpace of 2 by 3 dense matrices over Integer Ring

Subdivisions are preserved when lifting:

sage: A.subdivide([], [1,1]); A
[1||2 3]
[4||5 6]
sage: A.lift()
[1||2 3]
[4||5 6]

>>> from sage.all import *
>>> A.subdivide([], [Integer(1),Integer(1)]); A
[1||2 3]
[4||5 6]
>>> A.lift()
[1||2 3]
[4||5 6]
matrix_from_columns(columns)[source]#

Return the matrix constructed from self using columns with indices in the columns list.

EXAMPLES:

sage: M = MatrixSpace(Integers(8),3,3)
sage: A = M(range(9)); A
[0 1 2]
[3 4 5]
[6 7 0]
sage: A.matrix_from_columns([2,1])
[2 1]
[5 4]
[0 7]

>>> from sage.all import *
>>> M = MatrixSpace(Integers(Integer(8)),Integer(3),Integer(3))
>>> A = M(range(Integer(9))); A
[0 1 2]
[3 4 5]
[6 7 0]
>>> A.matrix_from_columns([Integer(2),Integer(1)])
[2 1]
[5 4]
[0 7]
matrix_from_rows(rows)[source]#

Return the matrix constructed from self using rows with indices in the rows list.

EXAMPLES:

sage: M = MatrixSpace(Integers(8),3,3)
sage: A = M(range(9)); A
[0 1 2]
[3 4 5]
[6 7 0]
sage: A.matrix_from_rows([2,1])
[6 7 0]
[3 4 5]

>>> from sage.all import *
>>> M = MatrixSpace(Integers(Integer(8)),Integer(3),Integer(3))
>>> A = M(range(Integer(9))); A
[0 1 2]
[3 4 5]
[6 7 0]
>>> A.matrix_from_rows([Integer(2),Integer(1)])
[6 7 0]
[3 4 5]
matrix_from_rows_and_columns(rows, columns)[source]#

Return the matrix constructed from self from the given rows and columns.

EXAMPLES:

sage: M = MatrixSpace(Integers(8),3,3)
sage: A = M(range(9)); A
[0 1 2]
[3 4 5]
[6 7 0]
sage: A.matrix_from_rows_and_columns([1], [0,2])
[3 5]
sage: A.matrix_from_rows_and_columns([1,2], [1,2])
[4 5]
[7 0]

>>> from sage.all import *
>>> M = MatrixSpace(Integers(Integer(8)),Integer(3),Integer(3))
>>> A = M(range(Integer(9))); A
[0 1 2]
[3 4 5]
[6 7 0]
>>> A.matrix_from_rows_and_columns([Integer(1)], [Integer(0),Integer(2)])
[3 5]
>>> A.matrix_from_rows_and_columns([Integer(1),Integer(2)], [Integer(1),Integer(2)])
[4 5]
[7 0]

Note that row and column indices can be reordered or repeated:

sage: A.matrix_from_rows_and_columns([2,1], [2,1])
[0 7]
[5 4]

>>> from sage.all import *
>>> A.matrix_from_rows_and_columns([Integer(2),Integer(1)], [Integer(2),Integer(1)])
[0 7]
[5 4]

For example here we take from row 1 columns 2 then 0 twice, and do this 3 times:

sage: A.matrix_from_rows_and_columns([1,1,1],[2,0,0])
[5 3 3]
[5 3 3]
[5 3 3]

>>> from sage.all import *
>>> A.matrix_from_rows_and_columns([Integer(1),Integer(1),Integer(1)],[Integer(2),Integer(0),Integer(0)])
[5 3 3]
[5 3 3]
[5 3 3]

AUTHORS:

  • Jaap Spies (2006-02-18)

  • Didier Deshommes: some Pyrex speedups implemented

minpoly(var='x', algorithm='linbox', proof=None)[source]#

Returns the minimal polynomial of`` self``.

INPUT:

  • var – a variable name

  • algorithm – generic or linbox (default: linbox)

  • proof – (default: True); whether to provably return the true minimal polynomial; if False, we only guarantee to return a divisor of the minimal polynomial. There are also certainly cases where the computed results is frequently not exactly equal to the minimal polynomial (but is instead merely a divisor of it).

Warning

If proof=True, minpoly is insanely slow compared to proof=False. This matters since proof=True is the default, unless you first type proof.linear_algebra(False).

EXAMPLES:

sage: A = random_matrix(GF(17), 10, 10)
sage: B = copy(A)
sage: min_p = A.minimal_polynomial(proof=True)
sage: min_p(A) == 0
True
sage: B == A
True

sage: char_p = A.characteristic_polynomial()
sage: min_p.divides(char_p)
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(17)), Integer(10), Integer(10))
>>> B = copy(A)
>>> min_p = A.minimal_polynomial(proof=True)
>>> min_p(A) == Integer(0)
True
>>> B == A
True

>>> char_p = A.characteristic_polynomial()
>>> min_p.divides(char_p)
True

sage: A = random_matrix(GF(1214471), 10, 10)                                # needs sage.rings.finite_rings
sage: B = copy(A)
sage: min_p = A.minimal_polynomial(proof=True)
sage: min_p(A) == 0
True
sage: B == A
True

sage: char_p = A.characteristic_polynomial()
sage: min_p.divides(char_p)
True

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(1214471)), Integer(10), Integer(10))                                # needs sage.rings.finite_rings
>>> B = copy(A)
>>> min_p = A.minimal_polynomial(proof=True)
>>> min_p(A) == Integer(0)
True
>>> B == A
True

>>> char_p = A.characteristic_polynomial()
>>> min_p.divides(char_p)
True

EXAMPLES:

sage: R.<x>=GF(3)[]
sage: A = matrix(GF(3),2,[0,0,1,2])
sage: A.minpoly()
x^2 + x

sage: A.minpoly(proof=False) in [x, x+1, x^2+x]
True

>>> from sage.all import *
>>> R = GF(Integer(3))['x']; (x,) = R._first_ngens(1)
>>> A = matrix(GF(Integer(3)),Integer(2),[Integer(0),Integer(0),Integer(1),Integer(2)])
>>> A.minpoly()
x^2 + x

>>> A.minpoly(proof=False) in [x, x+Integer(1), x**Integer(2)+x]
True
randomize(density=1, nonzero=False)[source]#

Randomize density proportion of the entries of this matrix, leaving the rest unchanged.

INPUT:

  • density – Integer; proportion (roughly) to be considered

    for changes

  • nonzero – Bool (default: False); whether the new

    entries are forced to be non-zero

OUTPUT:

  • None, the matrix is modified in-space

EXAMPLES:

sage: A = matrix(GF(5), 5, 5, 0)
sage: total_count = 0
sage: from collections import defaultdict
sage: dic = defaultdict(Integer)
sage: def add_samples(density):
....:     global dic, total_count
....:     for _ in range(100):
....:         A = Matrix(GF(5), 5, 5, 0)
....:         A.randomize(density)
....:         for a in A.list():
....:             dic[a] += 1
....:             total_count += 1.0

sage: add_samples(1.0)
sage: while not all(abs(dic[a]/total_count - 1/5) < 0.01 for a in dic):
....:     add_samples(1.0)

sage: def add_sample(density):
....:     global density_sum, total_count
....:     total_count += 1.0
....:     density_sum += random_matrix(GF(5), 1000, 1000, density=density).density()

sage: density_sum = 0.0
sage: total_count = 0.0
sage: add_sample(0.5)
sage: expected_density = 1.0 - (999/1000)^500
sage: expected_density
0.3936...
sage: while abs(density_sum/total_count - expected_density) > 0.001:
....:     add_sample(0.5)

>>> from sage.all import *
>>> A = matrix(GF(Integer(5)), Integer(5), Integer(5), Integer(0))
>>> total_count = Integer(0)
>>> from collections import defaultdict
>>> dic = defaultdict(Integer)
>>> def add_samples(density):
...     global dic, total_count
...     for _ in range(Integer(100)):
...         A = Matrix(GF(Integer(5)), Integer(5), Integer(5), Integer(0))
...         A.randomize(density)
...         for a in A.list():
...             dic[a] += Integer(1)
...             total_count += RealNumber('1.0')

>>> add_samples(RealNumber('1.0'))
>>> while not all(abs(dic[a]/total_count - Integer(1)/Integer(5)) < RealNumber('0.01') for a in dic):
...     add_samples(RealNumber('1.0'))

>>> def add_sample(density):
...     global density_sum, total_count
...     total_count += RealNumber('1.0')
...     density_sum += random_matrix(GF(Integer(5)), Integer(1000), Integer(1000), density=density).density()

>>> density_sum = RealNumber('0.0')
>>> total_count = RealNumber('0.0')
>>> add_sample(RealNumber('0.5'))
>>> expected_density = RealNumber('1.0') - (Integer(999)/Integer(1000))**Integer(500)
>>> expected_density
0.3936...
>>> while abs(density_sum/total_count - expected_density) > RealNumber('0.001'):
...     add_sample(RealNumber('0.5'))

The matrix is updated instead of overwritten:

sage: def add_sample(density):
....:     global density_sum, total_count
....:     total_count += 1.0
....:     A = random_matrix(GF(5), 1000, 1000, density=density)
....:     A.randomize(density=density, nonzero=True)
....:     density_sum += A.density()

sage: density_sum = 0.0
sage: total_count = 0.0
sage: add_sample(0.5)
sage: expected_density = 1.0 - (999/1000)^1000
sage: expected_density
0.6323...
sage: while abs(density_sum/total_count - expected_density) > 0.001:
....:     add_sample(0.5)

sage: density_sum = 0.0
sage: total_count = 0.0
sage: add_sample(0.1)
sage: expected_density = 1.0 - (999/1000)^200
sage: expected_density
0.1813...
sage: while abs(density_sum/total_count - expected_density) > 0.001:
....:     add_sample(0.1)

>>> from sage.all import *
>>> def add_sample(density):
...     global density_sum, total_count
...     total_count += RealNumber('1.0')
...     A = random_matrix(GF(Integer(5)), Integer(1000), Integer(1000), density=density)
...     A.randomize(density=density, nonzero=True)
...     density_sum += A.density()

>>> density_sum = RealNumber('0.0')
>>> total_count = RealNumber('0.0')
>>> add_sample(RealNumber('0.5'))
>>> expected_density = RealNumber('1.0') - (Integer(999)/Integer(1000))**Integer(1000)
>>> expected_density
0.6323...
>>> while abs(density_sum/total_count - expected_density) > RealNumber('0.001'):
...     add_sample(RealNumber('0.5'))

>>> density_sum = RealNumber('0.0')
>>> total_count = RealNumber('0.0')
>>> add_sample(RealNumber('0.1'))
>>> expected_density = RealNumber('1.0') - (Integer(999)/Integer(1000))**Integer(200)
>>> expected_density
0.1813...
>>> while abs(density_sum/total_count - expected_density) > RealNumber('0.001'):
...     add_sample(RealNumber('0.1'))
rank()[source]#

Return the rank of this matrix.

EXAMPLES:

sage: A = random_matrix(GF(3), 100, 100)
sage: B = copy(A)
sage: _ = A.rank()
sage: B == A
True

sage: A = random_matrix(GF(3), 100, 100, density=0.01)
sage: A.transpose().rank() == A.rank()
True

sage: A = matrix(GF(3), 100, 100)
sage: A.rank()
0

>>> from sage.all import *
>>> A = random_matrix(GF(Integer(3)), Integer(100), Integer(100))
>>> B = copy(A)
>>> _ = A.rank()
>>> B == A
True

>>> A = random_matrix(GF(Integer(3)), Integer(100), Integer(100), density=RealNumber('0.01'))
>>> A.transpose().rank() == A.rank()
True

>>> A = matrix(GF(Integer(3)), Integer(100), Integer(100))
>>> A.rank()
0

Rank is not implemented over the integers modulo a composite yet.:

sage: M = matrix(Integers(4), 2, [2,2,2,2])
sage: M.rank()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 4'.

>>> from sage.all import *
>>> M = matrix(Integers(Integer(4)), Integer(2), [Integer(2),Integer(2),Integer(2),Integer(2)])
>>> M.rank()
Traceback (most recent call last):
...
NotImplementedError: Echelon form not implemented over 'Ring of integers modulo 4'.

sage: # needs sage.rings.finite_rings
sage: A = random_matrix(GF(16007), 100, 100)
sage: B = copy(A)
sage: A.rank()
100
sage: B == A
True
sage: MS = A.parent()
sage: MS(1) == ~A*A
True

>>> from sage.all import *
>>> # needs sage.rings.finite_rings
>>> A = random_matrix(GF(Integer(16007)), Integer(100), Integer(100))
>>> B = copy(A)
>>> A.rank()
100
>>> B == A
True
>>> MS = A.parent()
>>> MS(Integer(1)) == ~A*A
True
right_kernel_matrix(algorithm='linbox', basis='echelon')[source]#

Returns a matrix whose rows form a basis for the right kernel of self.

If the base ring is the ring of integers modulo a composite, the keyword arguments are ignored and the computation is delegated to Matrix_dense.right_kernel_matrix().

INPUT:

  • algorithm – (default: 'linbox') a parameter that is passed on to self.echelon_form, if computation of an echelon form is required; see that routine for allowable values

  • basis – (default: 'echelon') a keyword that describes the format of the basis returned, allowable values are:

    • 'echelon': the basis matrix is in echelon form

    • 'pivot': the basis matrix is such that the submatrix obtained

      by taking the columns that in self contain no pivots, is the identity matrix

    • 'computed': no work is done to transform the basis; in

      the current implementation the result is the negative of that returned by 'pivot'

OUTPUT:

A matrix X whose rows are a basis for the right kernel of self. This means that self * X.transpose() is a zero matrix.

The result is not cached, but the routine benefits when self is known to be in echelon form already.

EXAMPLES:

sage: M = matrix(GF(5),6,6,range(36))
sage: M.right_kernel_matrix(basis='computed')
[4 2 4 0 0 0]
[3 3 0 4 0 0]
[2 4 0 0 4 0]
[1 0 0 0 0 4]
sage: M.right_kernel_matrix(basis='pivot')
[1 3 1 0 0 0]
[2 2 0 1 0 0]
[3 1 0 0 1 0]
[4 0 0 0 0 1]
sage: M.right_kernel_matrix()
[1 0 0 0 0 4]
[0 1 0 0 1 3]
[0 0 1 0 2 2]
[0 0 0 1 3 1]
sage: M * M.right_kernel_matrix().transpose()
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]

>>> from sage.all import *
>>> M = matrix(GF(Integer(5)),Integer(6),Integer(6),range(Integer(36)))
>>> M.right_kernel_matrix(basis='computed')
[4 2 4 0 0 0]
[3 3 0 4 0 0]
[2 4 0 0 4 0]
[1 0 0 0 0 4]
>>> M.right_kernel_matrix(basis='pivot')
[1 3 1 0 0 0]
[2 2 0 1 0 0]
[3 1 0 0 1 0]
[4 0 0 0 0 1]
>>> M.right_kernel_matrix()
[1 0 0 0 0 4]
[0 1 0 0 1 3]
[0 0 1 0 2 2]
[0 0 0 1 3 1]
>>> M * M.right_kernel_matrix().transpose()
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
submatrix(row=0, col=0, nrows=-1, ncols=-1)[source]#

Return the matrix constructed from self using the specified range of rows and columns.

INPUT:

  • row, col – index of the starting row and column. Indices start at zero

  • nrows, ncols – (optional) number of rows and columns to take. If not provided, take all rows below and all columns to the right of the starting entry

See also

The functions matrix_from_rows(), matrix_from_columns(), and matrix_from_rows_and_columns() allow one to select arbitrary subsets of rows and/or columns.

EXAMPLES:

Take the \(3 \times 3\) submatrix starting from entry \((1,1)\) in a \(4 \times 4\) matrix:

sage: m = matrix(GF(17),4, [1..16])
sage: m.submatrix(1, 1)
[ 6  7  8]
[10 11 12]
[14 15 16]

>>> from sage.all import *
>>> m = matrix(GF(Integer(17)),Integer(4), (ellipsis_range(Integer(1),Ellipsis,Integer(16))))
>>> m.submatrix(Integer(1), Integer(1))
[ 6  7  8]
[10 11 12]
[14 15 16]

Same thing, except take only two rows:

sage: m.submatrix(1, 1, 2)
[ 6  7  8]
[10 11 12]

>>> from sage.all import *
>>> m.submatrix(Integer(1), Integer(1), Integer(2))
[ 6  7  8]
[10 11 12]

And now take only one column:

sage: m.submatrix(1, 1, 2, 1)
[ 6]
[10]

>>> from sage.all import *
>>> m.submatrix(Integer(1), Integer(1), Integer(2), Integer(1))
[ 6]
[10]

You can take zero rows or columns if you want:

sage: m.submatrix(0, 0, 0)
[]
sage: parent(m.submatrix(0, 0, 0))
Full MatrixSpace of 0 by 4 dense matrices over Finite Field of size 17

>>> from sage.all import *
>>> m.submatrix(Integer(0), Integer(0), Integer(0))
[]
>>> parent(m.submatrix(Integer(0), Integer(0), Integer(0)))
Full MatrixSpace of 0 by 4 dense matrices over Finite Field of size 17
transpose()[source]#

Return the transpose of self, without changing self.

EXAMPLES:

We create a matrix, compute its transpose, and note that the original matrix is not changed.

sage: M = MatrixSpace(GF(41),  2)
sage: A = M([1,2,3,4])
sage: B = A.transpose()
sage: B
[1 3]
[2 4]
sage: A
[1 2]
[3 4]

>>> from sage.all import *
>>> M = MatrixSpace(GF(Integer(41)),  Integer(2))
>>> A = M([Integer(1),Integer(2),Integer(3),Integer(4)])
>>> B = A.transpose()
>>> B
[1 3]
[2 4]
>>> A
[1 2]
[3 4]

.T is a convenient shortcut for the transpose:

sage: A.T
[1 3]
[2 4]

>>> from sage.all import *
>>> A.T
[1 3]
[2 4]

sage: A.subdivide(None, 1); A
[1|2]
[3|4]
sage: A.transpose()
[1 3]
[---]
[2 4]

>>> from sage.all import *
>>> A.subdivide(None, Integer(1)); A
[1|2]
[3|4]
>>> A.transpose()
[1 3]
[---]
[2 4]