pylops.MatrixMult¶

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

Matrix multiplication.

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

Parameters:	A : `numpy.ndarray` or `scipy.sparse` matrix 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.
Attributes:	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`)

Methods

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