pylops_distributed.MatrixMult

class pylops_distributed.MatrixMult(A, dims=None, compute=(False, False), todask=(False, False), dtype='float64')

Matrix multiplication.

Simple wrapper to dask.array.dot for an input matrix \(\mathbf{A}\).

Parameters:

A : dask.array.ndarray

Matrix.

dims : tuple, optional

Number of samples for each other dimension of model (model/data will be reshaped and A applied multiple times to each column of the model/data).

compute : tuple, optional

Compute the outcome of forward and adjoint or simply define the graph and return a dask.array

todask : tuple, optional

Apply dask.array.from_array to model and data before applying forward and adjoint respectively

dtype : str, optional

Type of elements in input array.

Notes

Refer to pylops.basicoperators.MatrixMult for implementation details.

Attributes:

shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

__init__(A[, dims, compute, todask, dtype])	Initialize this LinearOperator.
adjoint()	Hermitian adjoint.
apply_columns(cols)	Apply subset of columns of operator
cond([uselobpcg])	Condition number of linear operator.
conj()	Complex conjugate operator
div(y[, niter])	Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x)	Matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg])	Most significant eigenvalues of linear operator.
inv()	Return the inverse of \(\mathbf{A}\).
matmat(X)	Matrix-matrix multiplication.
matvec(x)	Matrix-vector multiplication.
rmatmat(X)	Adjoint matrix-matrix multiplication.
rmatvec(x)	Adjoint Matrix-vector multiplication.
todense()	Return dense matrix.
tosparse()	Return sparse matrix.
transpose()	Transpose this linear operator.

inv()

Return the inverse of \(\mathbf{A}\).

Returns:

Ainv : numpy.ndarray

Inverse matrix.