pylops.Conj

class pylops.Conj(dims, dtype='complex128')

Complex conjugate operator.

Return the complex conjugate of the input. It is self-adjoint.

Parameters:

dims : int or tuple

Number of samples for each dimension

dtype : str, optional

Type of elements in input array.

Notes

In forward mode:

\[y_{i} = \Re\{x_{i}\} - i\Im\{x_{i}\} \quad \forall i=0,\ldots,N-1\]

In adjoint mode:

\[x_{i} = \Re\{y_{i}\} - i\Im\{y_{i}\} \quad \forall i=0,\ldots,N-1\]

Attributes:

shape : tuple

Operator shape

explicit : bool

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

Methods

__init__(dims[, 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.Conj

Conj