SquareMatrix¶

class SquareMatrix(*args)¶

Real square matrix.

Parameters:

sizeint, $n > 0$ , optional: Matrix size. Default is 1.
valuessequence of float with size $n^2$ , optional: Values. OpenTURNS uses column-major ordering (like Fortran) for reshaping the flat list of values. Default creates a zero matrix.

Examples

Create a matrix

>>> import openturns as ot
>>> M = ot.SquareMatrix(2, range(2 * 2))
>>> print(M)
[[ 0 2 ]
 [ 1 3 ]]

Get or set terms

>>> print(M[0, 0])
0.0
>>> M[0, 0] = 1.0
>>> print(M[0, 0])
1.0
>>> print(M[:, 0])
[[ 1 ]
 [ 1 ]]

Create an openturns matrix from a square numpy 2d-array (or matrix, or 2d-list)…

>>> import numpy as np
>>> np_2d_array = np.array([[1.0, 2.0], [3.0, 4.0]])
>>> ot_matrix = ot.SquareMatrix(np_2d_array)

and back

>>> np_matrix = np.matrix(ot_matrix)

Basic linear algebra operations (provided the dimensions are compatible)

>>> A = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> B = ot.SquareMatrix(np.eye(2))
>>> C = ot.Matrix(3, 2, [1.0] * 3 * 2)
>>> print(A * B - C)
[[ 0 1 ]
 [ 2 3 ]
 [ 4 5 ]]
>>> A = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> print(A ** 2)
[[  7 10 ]
 [ 15 22 ]]

Methods

`clean`(threshold)	Set elements smaller than a threshold to zero.
`computeDeterminant`([keepIntact])	Compute the determinant.
`computeEV`([keepIntact])	Compute the eigenvalues decomposition (EVD).
`computeEigenValues`([keepIntact])	Compute eigenvalues.
`computeGram`([transpose])	Compute the associated Gram matrix.
`computeLargestEigenValueModule`(*args)	Compute the largest eigenvalue module.
`computeLogAbsoluteDeterminant`([keepIntact])	Compute the logarithm of the absolute value of the determinant.
`computeQR`([fullQR, keepIntact])	Compute the QR factorization.
`computeSVD`([fullSVD, keepIntact])	Compute the singular values decomposition (SVD).
`computeSingularValues`([keepIntact])	Compute the singular values.
`computeTrace`()	Compute the trace of the matrix.
`getClassName`()	Accessor to the object's name.
`getDimension`()	Accessor to the dimension (the number of rows).
`getId`()	Accessor to the object's id.
`getImplementation`()	Accessor to the underlying implementation.
`getName`()	Accessor to the object's name.
`getNbColumns`()	Accessor to the number of columns.
`getNbRows`()	Accessor to the number of rows.
`isDiagonal`()	Test whether the matrix is diagonal or not.
`isEmpty`()	Tell if the matrix is empty.
`reshape`(newRowDim, newColDim)	Reshape the matrix.
`reshapeInPlace`(newRowDim, newColDim)	Reshape the matrix, in place.
`setName`(name)	Accessor to the object's name.
`solveLinearSystem`(*args)	Solve a square linear system whose the present matrix is the operator.
`transpose`()	Transpose the matrix.

computeHadamardProduct
computeSumElements
getDiagonal
setDiagonal
squareElements

__init__(*args)¶

clean(threshold)¶

Set elements smaller than a threshold to zero.

Parameters:

thresholdfloat: Threshold for zeroing elements.

Returns:

cleaned_matrixMatrix: Input matrix with elements smaller than the threshold set to zero.

computeDeterminant(keepIntact=True)¶

Compute the determinant.

Parameters:

keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

determinantfloat: The square matrix determinant.

Examples

>>> import openturns as ot
>>> A = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> A.computeDeterminant()
-2.0

computeEV(keepIntact=True)¶

Compute the eigenvalues decomposition (EVD).

The eigenvalues decomposition of a square matrix $\mat{M}$ with size $n$ reads:

$\mat{M} = \mat{\Phi} \mat{\Lambda} \mat{\Phi}^{-1}$

where $\mat{\Lambda}$ is an $n \times n$ diagonal matrix and $\mat{\Phi}$ is an $n \times n$ orthogonal matrix.

Parameters:

keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

eigen_valuesComplexCollection: The vector of eigenvalues with size $n$ that form the diagonal of the $n \times n$ matrix $\mat{\Lambda}$ of the EVD.
PhiSquareComplexMatrix: The left matrix of the EVD.

Notes

This uses LAPACK’S DGEEV.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> eigen_values, Phi = M.computeEV()
>>> Lambda = ot.SquareComplexMatrix(M.getDimension())
>>> for i in range(eigen_values.getSize()):
...     Lambda[i, i] = eigen_values[i]
>>> # from scipy.linalg import inv # SquareComplexMatrix does not implement solveLinearSystem
>>> # Phi, Lambda = np.matrix(Phi), np.matrix(Lambda)
>>> # np.testing.assert_array_almost_equal(Phi * Lambda * inv(Phi), M)

computeEigenValues(keepIntact=True)¶

Compute eigenvalues.

Parameters:

keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

eigenvaluesComplexCollection: Eigenvalues.

See also

computeEV, computeEigenValues

Examples

>>> import openturns as ot
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> M.computeLargestEigenValueModule()
5.3722...

computeLogAbsoluteDeterminant(keepIntact=True)¶

Compute the logarithm of the absolute value of the determinant.

Parameters:

keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

determinantfloat: The logarithm of the absolute value of the square matrix determinant.
signfloat: The sign of the determinant.

Examples

>>> import openturns as ot
>>> A = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> A.computeLogAbsoluteDeterminant()
[0.693147..., -1.0]

computeQR(fullQR=False, keepIntact=True)¶

Compute the QR factorization. By default, it is the economic decomposition which is computed.

The economic QR factorization of a rectangular matrix $\mat{M}$ with $n_r \geq n_c$ (more rows than columns) is defined as follows:

$\mat{M} = \mat{Q} \mat{R} = \mat{Q} \begin{bmatrix} \mat{R_1} \\ \mat{0} \end{bmatrix} = \begin{bmatrix} \mat{Q_1}, \mat{Q_2} \end{bmatrix} \begin{bmatrix} \mat{R_1} \\ \mat{0} \end{bmatrix} = \mat{Q_1} \mat{R_1}$

where $\mat{R_1}$ is an $n_c \times n_c$ upper triangular matrix, $\mat{Q_1}$ is $n_r \times n_c$ , $\mat{Q_2}$ is $n_r \times (n_r - n_c)$ , and $\mat{Q_1}$ and $\mat{Q_2}$ both have orthogonal columns.

Parameters:

full_qrbool, optional: A flag telling whether Q, R or Q1, R1 are returned. Default is False and returns Q1, R1.
keep_intactbool, optional: A flag telling whether the present matrix is preserved or not in the computation of the decomposition. Default is True and leaves the present matrix unchanged.

Returns:

Q1Matrix: The orthogonal matrix of the economic QR factorization.
R1TriangularMatrix: The right (upper) triangular matrix of the economic QR factorization.
QMatrix: The orthogonal matrix of the full QR factorization.
RTriangularMatrix: The right (upper) triangular matrix of the full QR factorization.

Notes

The economic QR factorization is often used for solving overdetermined linear systems (where the operator $\mat{M}$ has $n_r \geq n_c$ ) in the least-square sense because it implies solving a (simple) triangular system:

$\vect{\hat{x}} = \arg\min\limits_{\vect{x} \in \Rset^{n_r}} \|\mat{M} \vect{x} - \vect{b}\| = \mat{R_1}^{-1} (\Tr{\mat{Q_1}} \vect{b})$

This uses LAPACK’s DGEQRF and DORGQR.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> Q1, R1 = M.computeQR()
>>> np.testing.assert_array_almost_equal(Q1 * R1, M)

computeSVD(fullSVD=False, keepIntact=True)¶

Compute the singular values decomposition (SVD).

The singular values decomposition of a rectangular matrix $\mat{M}$ with size $n_r > n_c$ reads:

$\mat{M} = \mat{U} \mat{\Sigma} \Tr{\mat{V}}$

where $\mat{U}$ is an $n_r \times n_r$ orthogonal matrix, $\mat{\Sigma}$ is an $n_r \times n_c$ diagonal matrix and $\mat{V}$ is an $n_c \times n_c$ orthogonal matrix.

Parameters:

fullSVDbool, optional: Whether the null parts of the orthogonal factors are explicitly stored or not. Default is False and computes a reduced SVD.
keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

singular_valuesPoint: The vector of singular values with size $n = \min(n_r, n_c)$ that form the diagonal of the $n_r \times n_c$ matrix $\mat{\Sigma}$ of the SVD.
USquareMatrix: The left orthogonal matrix of the SVD.
VTSquareMatrix: The transposed right orthogonal matrix of the SVD.

Notes

This uses LAPACK’s DGESDD.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> singular_values, U, VT = M.computeSVD(True)
>>> Sigma = ot.Matrix(M.getNbRows(), M.getNbColumns())
>>> for i in range(singular_values.getSize()):
...     Sigma[i, i] = singular_values[i]
>>> np.testing.assert_array_almost_equal(U * Sigma * VT, M)

computeSingularValues(keepIntact=True)¶

Compute the singular values.

Parameters:

fullSVDbool, optional: Whether the null parts of the orthogonal factors are explicitly stored or not. Default is False and computes a reduced SVD.
keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

singular_valuesPoint: The vector of singular values with size $n = \min(n_r, n_c)$ that form the diagonal of the $n_r \times n_c$ matrix $\mat{\Sigma}$ of the SVD decomposition.

See also

computeSVD

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M.computeSingularValues(True))
[9.52552,0.514301]

computeTrace()¶

Compute the trace of the matrix.

Returns:

tracefloat: The trace of the matrix.

Examples

>>> import openturns as ot
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> M.computeTrace()
5.0

getClassName()¶

Accessor to the object’s name.

Returns:

class_namestr: The object class name (object.__class__.__name__).

getDimension()¶

Accessor to the dimension (the number of rows).

Returns:

dimensionint

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getImplementation()¶

Accessor to the underlying implementation.

Returns:

implImplementation: A copy of the underlying implementation object.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getNbColumns()¶

Accessor to the number of columns.

Returns:

n_columnsint

getNbRows()¶

Accessor to the number of rows.

Returns:

n_rowsint

isDiagonal()¶

Test whether the matrix is diagonal or not.

Returns:

testbool: Answer.

isEmpty()¶

Tell if the matrix is empty.

Returns:

is_emptybool: True if the matrix contains no element.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[]])
>>> M.isEmpty()
True

reshape(newRowDim, newColDim)¶

Reshape the matrix.

Parameters:

newRowDimint: The row dimension of the reshaped matrix.
newColDimint: The column dimension of the reshaped matrix.

Returns:

MTMatrix: The reshaped matrix.

Notes

If the size of the reshaped matrix is smaller than the size of the matrix to be reshaped, only the $newRowDim\times newColDim$ first elements are kept (in a column-major storage sense). If the size is greater, the new elements are set to zero.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M)
[[ 1 2 ]
 [ 3 4 ]
 [ 5 6 ]]
>>> print(M.reshape(1, 6))
1x6
[[ 1 3 5 2 4 6 ]]
>>> print(M.reshape(2, 2))
[[ 1 5 ]
 [ 3 2 ]]
>>> print(M.reshape(2, 6))
2x6
[[ 1 5 4 0 0 0 ]
 [ 3 2 6 0 0 0 ]]

reshapeInPlace(newRowDim, newColDim)¶

Reshape the matrix, in place.

Parameters:

newRowDimint: The row dimension of the reshaped matrix.
newColDimint: The column dimension of the reshaped matrix.

Notes

If the size of the reshaped matrix is smaller than the size of the matrix to be reshaped, only the $newRowDim\times newColDim$ first elements are kept (in a column-major storage sense). If the size is greater, the new elements are set to zero. If the size is unchanged, no copy of data is done.

Examples

>>> import openturns as ot
>>> M = ot.Matrix([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>> print(M)
[[ 1 2 ]
 [ 3 4 ]
 [ 5 6 ]]
>>> M.reshapeInPlace(1, 6)
>>> print(M)
1x6
[[ 1 3 5 2 4 6 ]]
>>> M.reshapeInPlace(2, 2)
>>> print(M)
[[ 1 5 ]
 [ 3 2 ]]
>>> M.reshapeInPlace(2, 6)
>>> print(M)
2x6
[[ 1 5 0 0 0 0 ]
 [ 3 2 0 0 0 0 ]]

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

solveLinearSystem(*args)¶

Solve a square linear system whose the present matrix is the operator.

Parameters:

rhssequence of float or Matrix with $n_r$ values or rows, respectively: The right hand side member of the linear system.
keep_intactbool, optional: A flag telling whether the present matrix can be overwritten or not. Default is True and leaves the present matrix unchanged.

Returns:

solutionPoint or Matrix: The solution of the square linear system.

Notes

This will handle both matrices and vectors. Note that you’d better type explicitly the matrix if it has some properties that could simplify the resolution (see TriangularMatrix).

This uses LAPACK’S DGESV for matrices and DGELSY for vectors.

Examples

>>> import openturns as ot
>>> import numpy as np
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> b = ot.Point([1.0] * 2)
>>> x = M.solveLinearSystem(b)
>>> np.testing.assert_array_almost_equal(M * x, b)

transpose()¶

Transpose the matrix.

Returns:

MTSquareMatrix: The transposed matrix.

Examples

>>> import openturns as ot
>>> M = ot.SquareMatrix([[1.0, 2.0], [3.0, 4.0]])
>>> print(M)
[[ 1 2 ]
 [ 3 4 ]]
>>> print(M.transpose())
[[ 1 3 ]
 [ 2 4 ]]