pylops.Zero¶
-
class
pylops.Zero(N, M=None, dtype='float64', name='Z')[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.
- name :
str, 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: 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. - N :
