pylops.Zero

class pylops.Zero(N, M=None, dtype='float64')

Zero operator.

Transform model into array of zeros of size \(N\) in forward and transform data into array of zeros of size \(N\) in adjoint.

Parameters:

N : int

Number of samples in data (and model in M is not provided).

M : int, optional

Number of samples in model.

dtype : str, optional

Type of elements in input array.

Notes

An Zero operator simply creates a null data vector \(\mathbf{y}\) in forward mode:

\[\mathbf{0} \mathbf{x} = \mathbf{0}_N\]

and a null model vector \(\mathbf{x}\) in forward mode:

\[\mathbf{0} \mathbf{y} = \mathbf{0}_M\]

Attributes:

shape : tuple

Operator shape

explicit : bool

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

Methods

__init__(self, N[, M, 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.
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.

Examples using pylops.Zero

Zero