# MatrixMult¶

class probnum.linops.MatrixMult(*args, **kwargs)

Bases: scipy.sparse.linalg.interface.MatrixLinearOperator, probnum.linops.LinearOperator

A linear operator defined via a matrix.

Parameters

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

Attributes Summary

 H Hermitian adjoint. T Transpose this linear operator. ndim

Methods Summary

 Call self as a function. Hermitian adjoint. cond([p]) Compute the condition number of the linear operator. Determinant of the linear operator. Matrix-matrix or matrix-vector multiplication. Eigenvalue spectrum of the linear operator. Inverse of the linear operator. Log absolute determinant of the linear operator. Matrix-matrix multiplication. Matrix-vector multiplication. Rank of the linear operator. Adjoint matrix-matrix multiplication. Adjoint matrix-vector multiplication. Dense matrix representation of the linear operator. Trace of the linear operator. Transpose this linear operator.

Attributes Documentation

H

Returns the Hermitian adjoint of self, aka the Hermitian conjugate or Hermitian transpose. For a complex matrix, the Hermitian adjoint is equal to the conjugate transpose.

Can be abbreviated self.H instead of self.adjoint().

Returns

A_H – Hermitian adjoint of self.

Return type

LinearOperator

T

Transpose this linear operator.

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

ndim = 2

Methods Documentation

__call__(x)

Call self as a function.

adjoint()

Returns the Hermitian adjoint of self, aka the Hermitian conjugate or Hermitian transpose. For a complex matrix, the Hermitian adjoint is equal to the conjugate transpose.

Can be abbreviated self.H instead of self.adjoint().

Returns

A_H – Hermitian adjoint of self.

Return type

LinearOperator

cond(p=None)[source]

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

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

Return type

{float, inf}

det()[source]

Determinant of the linear operator.

dot(x)

Matrix-matrix or matrix-vector multiplication.

Parameters

x (array_like) – 1-d or 2-d array, representing a vector or matrix.

Returns

Ax – 1-d or 2-d array (depending on the shape of x) that represents the result of applying this linear operator on x.

Return type

array

eigvals()[source]

Eigenvalue spectrum of the linear operator.

inv()[source]

Inverse of the linear operator.

logabsdet()[source]

Log absolute determinant of the linear operator.

matmat(X)

Matrix-matrix multiplication.

Performs the operation y=A*X where A is an MxN linear operator and X dense N*K matrix or ndarray.

Parameters

X ({matrix, ndarray}) – An array with shape (N,K).

Returns

Y – A matrix or ndarray with shape (M,K) depending on the type of the X argument.

Return type

{matrix, ndarray}

Notes

This matmat wraps any user-specified matmat routine or overridden _matmat method to ensure that y has the correct type.

matvec(x)

Matrix-vector multiplication. Performs the operation y=A*x where A is an MxN linear operator and x is a 1-d array or random variable.

Parameters

x ({matrix, ndarray, RandomVariable}) – An array or RandomVariable with shape (N,) or (N,1).

Returns

y – A matrix or ndarray or RandomVariable with shape (M,) or (M,1) depending on the type and shape of the x argument.

Return type

{matrix, ndarray}

Notes

This matvec wraps the user-specified matvec routine or overridden _matvec method to ensure that y has the correct shape and type.

rank()[source]

Rank of the linear operator.

rmatmat(X)

Performs the operation y = A^H * x where A is an MxN linear operator and x is a column vector or 1-d array, or 2-d array. The default implementation defers to the adjoint.

Parameters

X ({matrix, ndarray}) – A matrix or 2D array.

Returns

Y – A matrix or 2D array depending on the type of the input.

Return type

{matrix, ndarray}

Notes

This rmatmat wraps the user-specified rmatmat routine.

rmatvec(x)

Performs the operation y = A^H * x where A is an MxN linear operator and x is a column vector or 1-d array.

Parameters

x ({matrix, ndarray}) – An array with shape (M,) or (M,1).

Returns

y – A matrix or ndarray with shape (N,) or (N,1) depending on the type and shape of the x argument.

Return type

{matrix, ndarray}

Notes

This rmatvec wraps the user-specified rmatvec routine or overridden _rmatvec method to ensure that y has the correct shape and type.

todense()[source]

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()[source]

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

transpose()

Transpose this linear operator.

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