class pylops.MatrixMult(A, dims=None, dtype='float64')[source]

Matrix multiplication.

Simple wrapper to and numpy.vdot for an input matrix \(\mathbf{A}\).

A : numpy.ndarray or scipy.sparse 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).

dtype : str, optional

Type of elements in input array.

shape : tuple

Operator shape

explicit : bool

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

complex : bool

Matrix has complex numbers (True) or not (False)


__init__(A[, dims, 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-matrix or 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) Matrix-matrix multiplication.
rmatvec(x) Adjoint matrix-vector multiplication.
todense([backend]) Return dense matrix.
toimag([forw, adj]) Imag operator
toreal([forw, adj]) Real operator
tosparse() Return sparse matrix.
transpose() Transpose this linear operator.

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

Ainv : numpy.ndarray

Inverse matrix.