Base class for sparse matrices

Base class for sparse matrices

class sage.matrix.matrix_sparse.Matrix_sparse

Bases: sage.matrix.matrix.Matrix

The initialization routine of the Matrix base class ensures that it sets the attributes self._parent, self._base_ring, self._nrows, self._ncols. It sets the latter ones by accessing the relevant information on parent, which is often slower than what a more specific subclass can do.

Subclasses of Matrix can safely skip calling Matrix.__init__ provided they take care of initializing these attributes themselves.

The private attributes self._is_immutable and self._cache are implicitly initialized to valid values upon memory allocation.

EXAMPLES:

sage: import sage.matrix.matrix0
sage: A = sage.matrix.matrix0.Matrix(MatrixSpace(QQ,2))
sage: type(A)
<type 'sage.matrix.matrix0.Matrix'>
antitranspose()
apply_map(phi, R=None, sparse=True)

Apply the given map phi (an arbitrary Python function or callable object) to this matrix. If R is not given, automatically determine the base ring of the resulting matrix.

INPUT:
sparse – False to make the output a dense matrix; default True
  • phi - arbitrary Python function or callable object
  • R - (optional) ring

OUTPUT: a matrix over R

EXAMPLES:

sage: m = matrix(ZZ, 10000, {(1,2): 17}, sparse=True)
sage: k.<a> = GF(9)
sage: f = lambda x: k(x)
sage: n = m.apply_map(f)
sage: n.parent()
Full MatrixSpace of 10000 by 10000 sparse matrices over Finite Field in a of size 3^2
sage: n[1,2]
2

An example where the codomain is explicitly specified.

sage: n = m.apply_map(lambda x:x%3, GF(3))
sage: n.parent()
Full MatrixSpace of 10000 by 10000 sparse matrices over Finite Field of size 3
sage: n[1,2]
2

If we didn’t specify the codomain, the resulting matrix in the above case ends up over ZZ again:

sage: n = m.apply_map(lambda x:x%3)
sage: n.parent()
Full MatrixSpace of 10000 by 10000 sparse matrices over Integer Ring
sage: n[1,2]
2

If self is subdivided, the result will be as well:

sage: m = matrix(2, 2, [0, 0, 3, 0])
sage: m.subdivide(None, 1); m
[0|0]
[3|0]
sage: m.apply_map(lambda x: x*x)
[0|0]
[9|0]

If the map sends zero to a non-zero value, then it may be useful to get the result as a dense matrix.

sage: m = matrix(ZZ, 3, 3, [0] * 7 + [1,2], sparse=True); m
[0 0 0]
[0 0 0]
[0 1 2]
sage: parent(m)
Full MatrixSpace of 3 by 3 sparse matrices over Integer Ring
sage: n = m.apply_map(lambda x: x+polygen(QQ), sparse=False); n
[    x     x     x]
[    x     x     x]
[    x x + 1 x + 2]
sage: parent(n)
Full MatrixSpace of 3 by 3 dense matrices over Univariate Polynomial Ring in x over Rational Field

TESTS:

sage: m = matrix([], sparse=True)
sage: m.apply_map(lambda x: x*x) == m
True

sage: m.apply_map(lambda x: x*x, sparse=False).parent()
Full MatrixSpace of 0 by 0 dense matrices over Integer Ring

Check that we don’t unnecessarily apply phi to 0 in the sparse case:

sage: m = matrix(QQ, 2, 2, range(1, 5), sparse=True)
sage: m.apply_map(lambda x: 1/x)
[  1 1/2]
[1/3 1/4]

Test subdivisions when phi maps 0 to non-zero:

sage: m = matrix(2, 2, [0, 0, 3, 0])
sage: m.subdivide(None, 1); m
[0|0]
[3|0]
sage: m.apply_map(lambda x: x+1)
[1|1]
[4|1]
apply_morphism(phi)

Apply the morphism phi to the coefficients of this sparse matrix.

The resulting matrix is over the codomain of phi.

INPUT:

  • phi - a morphism, so phi is callable and phi.domain() and phi.codomain() are defined. The codomain must be a ring.

OUTPUT: a matrix over the codomain of phi

EXAMPLES:

sage: m = matrix(ZZ, 3, range(9), sparse=True)
sage: phi = ZZ.hom(GF(5))
sage: m.apply_morphism(phi)
[0 1 2]
[3 4 0]
[1 2 3]
sage: m.apply_morphism(phi).parent()
Full MatrixSpace of 3 by 3 sparse matrices over Finite Field of size 5
augment(right, subdivide=False)

Return the augmented matrix of the form:

[self | right].

EXAMPLES:

sage: M = MatrixSpace(QQ, 2, 2, sparse=True)
sage: A = M([1,2, 3,4])
sage: A
[1 2]
[3 4]
sage: N = MatrixSpace(QQ, 2, 1, sparse=True)
sage: B = N([9,8])
sage: B
[9]
[8]
sage: A.augment(B)
[1 2 9]
[3 4 8]
sage: B.augment(A)
[9 1 2]
[8 3 4]

A vector may be augmented to a matrix.

sage: A = matrix(QQ, 3, 4, range(12), sparse=True)
sage: v = vector(QQ, 3, range(3), sparse=True)
sage: A.augment(v)
[ 0  1  2  3  0]
[ 4  5  6  7  1]
[ 8  9 10 11  2]

The subdivide option will add a natural subdivision between self and right. For more details about how subdivisions are managed when augmenting, see sage.matrix.matrix1.Matrix.augment().

sage: A = matrix(QQ, 3, 5, range(15), sparse=True)
sage: B = matrix(QQ, 3, 3, range(9), sparse=True)
sage: A.augment(B, subdivide=True)
[ 0  1  2  3  4| 0  1  2]
[ 5  6  7  8  9| 3  4  5]
[10 11 12 13 14| 6  7  8]

TESTS:

Verify that Trac #12689 is fixed:

sage: A = identity_matrix(QQ, 2, sparse=True)
sage: B = identity_matrix(ZZ, 2, sparse=True)
sage: A.augment(B)
[1 0 1 0]
[0 1 0 1]
change_ring(ring)

Return the matrix obtained by coercing the entries of this matrix into the given ring.

Always returns a copy (unless self is immutable, in which case returns self).

EXAMPLES:

sage: A = matrix(QQ['x,y'], 2, [0,-1,2*x,-2], sparse=True); A
[  0  -1]
[2*x  -2]
sage: A.change_ring(QQ['x,y,z'])
[  0  -1]
[2*x  -2]

Subdivisions are preserved when changing rings:

sage: A.subdivide([2],[]); A
[  0  -1]
[2*x  -2]
[-------]
sage: A.change_ring(RR['x,y'])
[                 0  -1.00000000000000]
[2.00000000000000*x  -2.00000000000000]
[-------------------------------------]
charpoly(var='x', **kwds)

Return the characteristic polynomial of this matrix.

Note - the generic sparse charpoly implementation in Sage is to just compute the charpoly of the corresponding dense matrix, so this could use a lot of memory. In particular, for this matrix, the charpoly will be computed using a dense algorithm.

EXAMPLES:

sage: A = matrix(ZZ, 4, range(16), sparse=True)
sage: A.charpoly()
x^4 - 30*x^3 - 80*x^2
sage: A.charpoly('y')
y^4 - 30*y^3 - 80*y^2
sage: A.charpoly()
x^4 - 30*x^3 - 80*x^2
density()

Return the density of the matrix.

By density we understand the ratio of the number of nonzero positions and the self.nrows() * self.ncols(), i.e. the number of possible nonzero positions.

EXAMPLES:

sage: a = matrix([[],[],[],[]], sparse=True); a.density()
0
sage: a = matrix(5000,5000,{(1,2): 1}); a.density()
1/25000000
determinant(**kwds)

Return the determinant of this matrix.

Note

the generic sparse determinant implementation in Sage is to just compute the determinant of the corresponding dense matrix, so this could use a lot of memory. In particular, for this matrix, the determinant will be computed using a dense algorithm.

EXAMPLES:

sage: A = matrix(ZZ, 4, range(16), sparse=True)
sage: B = A + identity_matrix(ZZ, 4, sparse=True)
sage: B.det()
-49
matrix_from_rows_and_columns(rows, columns)

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

EXAMPLES:

sage: M = MatrixSpace(Integers(8),3,3, sparse=True)
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]

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]

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]

We can efficiently extract large submatrices:

sage: A = random_matrix(ZZ, 100000, density=.00005, sparse=True)  # long time (4s on sage.math, 2012)
sage: B = A[50000:,:50000]        # long time
sage: len(B.nonzero_positions())  # long time
17550              # 32-bit
125449             # 64-bit

We must pass in a list of indices:

sage: A=random_matrix(ZZ,100,density=.02,sparse=True)
sage: A.matrix_from_rows_and_columns(1,[2,3])
Traceback (most recent call last):
...
TypeError: rows must be a list of integers
sage: A.matrix_from_rows_and_columns([1,2],3)
Traceback (most recent call last):
...
TypeError: columns must be a list of integers

AUTHORS:

  • Jaap Spies (2006-02-18)
  • Didier Deshommes: some Pyrex speedups implemented
  • Jason Grout: sparse matrix optimizations
stack(bottom, subdivide=False)

Stack self on top of bottom.

[ self ] [ bottom ]

EXAMPLES:

sage: M = Matrix(QQ, 2, 3, range(6), sparse=True)
sage: N = Matrix(QQ, 1, 3, [10,11,12], sparse=True)
sage: M.stack(N)
[ 0  1  2]
[ 3  4  5]
[10 11 12]

A vector may be stacked below a matrix.

sage: A = matrix(QQ, 2, 5, range(10), sparse=True)
sage: v = vector(QQ, 5, range(5), sparse=True)
sage: A.stack(v)
[0 1 2 3 4]
[5 6 7 8 9]
[0 1 2 3 4]

The subdivide option will add a natural subdivision between self and other. For more details about how subdivisions are managed when stacking, see sage.matrix.matrix1.Matrix.stack().

sage: A = matrix(ZZ, 3, 4, range(12), sparse=True)
sage: B = matrix(ZZ, 2, 4, range(8), sparse=True)
sage: A.stack(B, subdivide=True)
[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[-----------]
[ 0  1  2  3]
[ 4  5  6  7]
transpose()

Returns 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(QQ,  2, sparse=True)
sage: A = M([1,2,3,4])
sage: B = A.transpose()
sage: print B
[1 3]
[2 4]
sage: print A
[1 2]
[3 4]

.T is a convenient shortcut for the transpose:

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

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