Sparse Matrices

SparseMatrix Class Reference

class sympy.matrices.sparse.SparseMatrix[source]

A sparse matrix (a matrix with a large number of zero elements).

Examples

>>> from sympy.matrices import SparseMatrix
>>> SparseMatrix(2, 2, range(4))
Matrix([
[0, 1],
[2, 3]])
>>> SparseMatrix(2, 2, {(1, 1): 2})
Matrix([
[0, 0],
[0, 2]])

See also

sympy.matrices.dense.Matrix

CL

Alternate faster representation

LDLdecomposition()[source]

Returns the LDL Decomposition (matrices L and D) of matrix A, such that L * D * L.T == A. A must be a square, symmetric, positive-definite and non-singular.

This method eliminates the use of square root and ensures that all the diagonal entries of L are 1.

Examples

>>> from sympy.matrices import SparseMatrix
>>> A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
>>> L, D = A.LDLdecomposition()
>>> L
Matrix([
[   1,   0, 0],
[ 3/5,   1, 0],
[-1/5, 1/3, 1]])
>>> D
Matrix([
[25, 0, 0],
[ 0, 9, 0],
[ 0, 0, 9]])
>>> L * D * L.T == A
True
RL

Alternate faster representation

applyfunc(f)[source]

Apply a function to each element of the matrix.

Examples

>>> from sympy.matrices import SparseMatrix
>>> m = SparseMatrix(2, 2, lambda i, j: i*2+j)
>>> m
Matrix([
[0, 1],
[2, 3]])
>>> m.applyfunc(lambda i: 2*i)
Matrix([
[0, 2],
[4, 6]])
as_immutable()[source]

Returns an Immutable version of this Matrix.

as_mutable()[source]

Returns a mutable version of this matrix.

Examples

>>> from sympy import ImmutableMatrix
>>> X = ImmutableMatrix([[1, 2], [3, 4]])
>>> Y = X.as_mutable()
>>> Y[1, 1] = 5 # Can set values in Y
>>> Y
Matrix([
[1, 2],
[3, 5]])
cholesky()[source]

Returns the Cholesky decomposition L of a matrix A such that L * L.T = A

A must be a square, symmetric, positive-definite and non-singular matrix

Examples

>>> from sympy.matrices import SparseMatrix
>>> A = SparseMatrix(((25,15,-5),(15,18,0),(-5,0,11)))
>>> A.cholesky()
Matrix([
[ 5, 0, 0],
[ 3, 3, 0],
[-1, 1, 3]])
>>> A.cholesky() * A.cholesky().T == A
True
col_list()[source]

Returns a column-sorted list of non-zero elements of the matrix.

Examples

>>> from sympy.matrices import SparseMatrix
>>> a=SparseMatrix(((1, 2), (3, 4)))
>>> a
Matrix([
[1, 2],
[3, 4]])
>>> a.CL
[(0, 0, 1), (1, 0, 3), (0, 1, 2), (1, 1, 4)]

See also

col_op, row_list

liupc()[source]

Liu’s algorithm, for pre-determination of the Elimination Tree of the given matrix, used in row-based symbolic Cholesky factorization.

Examples

>>> from sympy.matrices import SparseMatrix
>>> S = SparseMatrix([
... [1, 0, 3, 2],
... [0, 0, 1, 0],
... [4, 0, 0, 5],
... [0, 6, 7, 0]])
>>> S.liupc()
([[0], [], [0], [1, 2]], [4, 3, 4, 4])

References

Symbolic Sparse Cholesky Factorization using Elimination Trees, Jeroen Van Grondelle (1999) http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.7582

nnz()[source]

Returns the number of non-zero elements in Matrix.

row_list()[source]

Returns a row-sorted list of non-zero elements of the matrix.

Examples

>>> from sympy.matrices import SparseMatrix
>>> a = SparseMatrix(((1, 2), (3, 4)))
>>> a
Matrix([
[1, 2],
[3, 4]])
>>> a.RL
[(0, 0, 1), (0, 1, 2), (1, 0, 3), (1, 1, 4)]

See also

row_op, col_list

row_structure_symbolic_cholesky()[source]

Symbolic cholesky factorization, for pre-determination of the non-zero structure of the Cholesky factororization.

Examples

>>> from sympy.matrices import SparseMatrix
>>> S = SparseMatrix([
... [1, 0, 3, 2],
... [0, 0, 1, 0],
... [4, 0, 0, 5],
... [0, 6, 7, 0]])
>>> S.row_structure_symbolic_cholesky()
[[0], [], [0], [1, 2]]

References

Symbolic Sparse Cholesky Factorization using Elimination Trees, Jeroen Van Grondelle (1999) http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.7582

scalar_multiply(scalar)[source]

Scalar element-wise multiplication

solve(rhs, method='LDL')[source]

Return solution to self*soln = rhs using given inversion method.

For a list of possible inversion methods, see the .inv() docstring.

solve_least_squares(rhs, method='LDL')[source]

Return the least-square fit to the data.

By default the cholesky_solve routine is used (method=’CH’); other methods of matrix inversion can be used. To find out which are available, see the docstring of the .inv() method.

Examples

>>> from sympy.matrices import SparseMatrix, Matrix, ones
>>> A = Matrix([1, 2, 3])
>>> B = Matrix([2, 3, 4])
>>> S = SparseMatrix(A.row_join(B))
>>> S
Matrix([
[1, 2],
[2, 3],
[3, 4]])

If each line of S represent coefficients of Ax + By and x and y are [2, 3] then S*xy is:

>>> r = S*Matrix([2, 3]); r
Matrix([
[ 8],
[13],
[18]])

But let’s add 1 to the middle value and then solve for the least-squares value of xy:

>>> xy = S.solve_least_squares(Matrix([8, 14, 18])); xy
Matrix([
[ 5/3],
[10/3]])

The error is given by S*xy - r:

>>> S*xy - r
Matrix([
[1/3],
[1/3],
[1/3]])
>>> _.norm().n(2)
0.58

If a different xy is used, the norm will be higher:

>>> xy += ones(2, 1)/10
>>> (S*xy - r).norm().n(2)
1.5
class sympy.matrices.sparse.MutableSparseMatrix[source]
col_del(k)[source]

Delete the given column of the matrix.

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix([[0, 0], [0, 1]])
>>> M
Matrix([
[0, 0],
[0, 1]])
>>> M.col_del(0)
>>> M
Matrix([
[0],
[1]])

See also

row_del

col_join(other)[source]

Returns B augmented beneath A (row-wise joining):

[A]
[B]

Examples

>>> from sympy import SparseMatrix, Matrix, ones
>>> A = SparseMatrix(ones(3))
>>> A
Matrix([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
>>> B = SparseMatrix.eye(3)
>>> B
Matrix([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
>>> C = A.col_join(B); C
Matrix([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
>>> C == A.col_join(Matrix(B))
True

Joining along columns is the same as appending rows at the end of the matrix:

>>> C == A.row_insert(A.rows, Matrix(B))
True
col_op(j, f)[source]

In-place operation on col j using two-arg functor whose args are interpreted as (self[i, j], i) for i in range(self.rows).

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix.eye(3)*2
>>> M[1, 0] = -1
>>> M.col_op(1, lambda v, i: v + 2*M[i, 0]); M
Matrix([
[ 2, 4, 0],
[-1, 0, 0],
[ 0, 0, 2]])
col_swap(i, j)[source]

Swap, in place, columns i and j.

Examples

>>> from sympy.matrices import SparseMatrix
>>> S = SparseMatrix.eye(3); S[2, 1] = 2
>>> S.col_swap(1, 0); S
Matrix([
[0, 1, 0],
[1, 0, 0],
[2, 0, 1]])
fill(value)[source]

Fill self with the given value.

Notes

Unless many values are going to be deleted (i.e. set to zero) this will create a matrix that is slower than a dense matrix in operations.

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix.zeros(3); M
Matrix([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])
>>> M.fill(1); M
Matrix([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
row_del(k)[source]

Delete the given row of the matrix.

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix([[0, 0], [0, 1]])
>>> M
Matrix([
[0, 0],
[0, 1]])
>>> M.row_del(0)
>>> M
Matrix([[0, 1]])

See also

col_del

row_join(other)[source]

Returns B appended after A (column-wise augmenting):

[A B]

Examples

>>> from sympy import SparseMatrix, Matrix
>>> A = SparseMatrix(((1, 0, 1), (0, 1, 0), (1, 1, 0)))
>>> A
Matrix([
[1, 0, 1],
[0, 1, 0],
[1, 1, 0]])
>>> B = SparseMatrix(((1, 0, 0), (0, 1, 0), (0, 0, 1)))
>>> B
Matrix([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
>>> C = A.row_join(B); C
Matrix([
[1, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 1]])
>>> C == A.row_join(Matrix(B))
True

Joining at row ends is the same as appending columns at the end of the matrix:

>>> C == A.col_insert(A.cols, B)
True
row_op(i, f)[source]

In-place operation on row i using two-arg functor whose args are interpreted as (self[i, j], j).

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix.eye(3)*2
>>> M[0, 1] = -1
>>> M.row_op(1, lambda v, j: v + 2*M[0, j]); M
Matrix([
[2, -1, 0],
[4,  0, 0],
[0,  0, 2]])

See also

row, zip_row_op, col_op

row_swap(i, j)[source]

Swap, in place, columns i and j.

Examples

>>> from sympy.matrices import SparseMatrix
>>> S = SparseMatrix.eye(3); S[2, 1] = 2
>>> S.row_swap(1, 0); S
Matrix([
[0, 1, 0],
[1, 0, 0],
[0, 2, 1]])
zip_row_op(i, k, f)[source]

In-place operation on row i using two-arg functor whose args are interpreted as (self[i, j], self[k, j]).

Examples

>>> from sympy.matrices import SparseMatrix
>>> M = SparseMatrix.eye(3)*2
>>> M[0, 1] = -1
>>> M.zip_row_op(1, 0, lambda v, u: v + 2*u); M
Matrix([
[2, -1, 0],
[4,  0, 0],
[0,  0, 2]])

See also

row, row_op, col_op

ImmutableSparseMatrix Class Reference

class sympy.matrices.immutable.ImmutableSparseMatrix[source]

Create an immutable version of a sparse matrix.

Examples

>>> from sympy import eye
>>> from sympy.matrices.immutable import ImmutableSparseMatrix
>>> ImmutableSparseMatrix(1, 1, {})
Matrix([[0]])
>>> ImmutableSparseMatrix(eye(3))
Matrix([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
>>> _[0, 0] = 42
Traceback (most recent call last):
...
TypeError: Cannot set values of ImmutableSparseMatrix
>>> _.shape
(3, 3)
is_diagonalizable(reals_only=False, **kwargs)[source]

Returns true if a matrix is diagonalizable.

Parameters

reals_only : bool. If reals_only=True, determine whether the matrix can be

diagonalized without complex numbers. (Default: False)

Kwargs

clear_cachebool. If True, clear the result of any computations when finished.

(Default: True)

Examples

>>> from sympy import Matrix
>>> m = Matrix(3, 3, [1, 2, 0, 0, 3, 0, 2, -4, 2])
>>> m
Matrix([
[1,  2, 0],
[0,  3, 0],
[2, -4, 2]])
>>> m.is_diagonalizable()
True
>>> m = Matrix(2, 2, [0, 1, 0, 0])
>>> m
Matrix([
[0, 1],
[0, 0]])
>>> m.is_diagonalizable()
False
>>> m = Matrix(2, 2, [0, 1, -1, 0])
>>> m
Matrix([
[ 0, 1],
[-1, 0]])
>>> m.is_diagonalizable()
True
>>> m.is_diagonalizable(reals_only=True)
False

See also

is_diagonal, diagonalize