# pylops.Zero¶

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.

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__(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.