Matrix¶

class probnum.linops.Matrix(A)

A linear operator defined via a matrix.

Parameters

A (array-like or scipy.sparse.spmatrix) – The explicit matrix.

Attributes Summary

 H Hermitian adjoint. T Transposed linear operator. dtype Data type of the linear operator. is_square Whether input dimension matches output dimension. ndim Number of linear operator dimensions. shape Shape of the linear operator. size rtype int

Methods Summary

 __call__(x[, axis]) Call self as a function. Hermitian adjoint. astype(dtype[, order, casting, subok, copy]) Cast a linear operator to a different dtype. broadcast_matmat(matmat) Broadcasting for a (implicitly defined) matrix-matrix product. broadcast_matvec(matvec) Broadcasting for a (implicitly defined) matrix-vector product. broadcast_rmatmat(rmatmat) rtype broadcast_rmatvec(rmatvec) rtype cond([p]) Compute the condition number of the linear operator. Complex conjugate linear operator. Complex conjugate linear operator. Determinant of the linear operator. Eigenvalue spectrum of the linear operator. Inverse of the linear operator. Log absolute determinant of the linear operator. Rank of the linear operator. todense([cache]) Dense matrix representation of the linear operator. Trace of the linear operator. Transpose this linear operator.

Attributes Documentation

H

Hermitian adjoint.

Return type

LinearOperator

T

Transposed linear operator.

Return type

LinearOperator

dtype

Data type of the linear operator.

Return type

dtype

is_square

Whether input dimension matches output dimension.

Return type

bool

ndim

Number of linear operator dimensions.

Defined analogously to numpy.ndarray.ndim.

Return type

int

shape

Shape of the linear operator.

Defined as a tuple of the output and input dimension of operator.

Return type
size
Return type

int

Methods Documentation

__call__(x, axis=None)

Call self as a function.

Return type

ndarray

adjoint()

Hermitian adjoint.

Return type

LinearOperator

astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Cast a linear operator to a different dtype.

Parameters
Return type

LinearOperator

classmethod broadcast_matmat(matmat)

Broadcasting for a (implicitly defined) matrix-matrix product.

Convenience function / decorator to broadcast the definition of a matrix-matrix product to vectors. This can be used to easily construct a new linear operator only from a matrix-matrix product.

Return type
classmethod broadcast_matvec(matvec)

Broadcasting for a (implicitly defined) matrix-vector product.

Convenience function / decorator to broadcast the definition of a matrix-vector product. This can be used to easily construct a new linear operator only from a matrix-vector product.

Return type
classmethod broadcast_rmatmat(rmatmat)
Return type
classmethod broadcast_rmatvec(rmatvec)
Return type
cond(p=None)

Compute the condition number of the linear operator.

The condition number of the linear operator with respect to the p norm. It measures how much the solution $$x$$ of the linear system $$Ax=b$$ changes with respect to small changes in $$b$$.

Parameters

p ({None, 1, , 2, , inf, 'fro'}, optional) –

Order of the norm:

p

norm for matrices

None

2-norm, computed directly via singular value decomposition

’fro’

Frobenius norm

np.inf

max(sum(abs(x), axis=1))

1

max(sum(abs(x), axis=0))

2

2-norm (largest sing. value)

Returns

The condition number of the linear operator. May be infinite.

Return type

cond

conj()

Complex conjugate linear operator.

Return type

LinearOperator

conjugate()

Complex conjugate linear operator.

Return type

LinearOperator

det()

Determinant of the linear operator.

Return type

inexact

eigvals()

Eigenvalue spectrum of the linear operator.

Return type

ndarray

inv()

Inverse of the linear operator.

Return type

LinearOperator

logabsdet()

Log absolute determinant of the linear operator.

Return type

inexact

rank()

Rank of the linear operator.

Return type

int64

todense(cache=True)

Dense matrix representation of the linear operator.

This method can be computationally very costly depending on the shape of the linear operator. Use with caution.

Returns

matrix – Matrix representation of the linear operator.

Return type

np.ndarray

trace()

Trace of the linear operator.

Computes the trace of a square linear operator $$\text{tr}(A) = \sum_{i-1}^n A_{ii}$$.

Returns

trace – Trace of the linear operator.

Return type

float

Raises

LinAlgError : – If trace() is called on a non-square matrix.

transpose()

Transpose this linear operator.

Can be abbreviated self.T instead of self.transpose().

Return type

LinearOperator