# pylops.Zero#

class pylops.Zero(*args, **kwargs)[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
Nint

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

Mint, optional

Number of samples in model.

dtypestr, optional

Type of elements in input array.

namestr, optional

New in version 2.0.0.

Name of operator (to be used by pylops.utils.describe.describe)

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
shapetuple

Operator shape

explicitbool

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

Methods

 __init__(N[, M, dtype, name]) 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. reset_count() Reset counters 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.

Zero

Zero