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

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.

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.


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:

\[\mathbf{0} \mathbf{y} = \mathbf{0}_M\]
shape : tuple

Operator shape

explicit : bool

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


__init__(N[, M, 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, densesolver]) 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.
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.
trace([neval, method, backend]) Trace of linear operator.
transpose() Transpose this linear operator.

Examples using pylops.Zero