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)
- N
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])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()
