pylops_gpu.Identity

class pylops_gpu.Identity(N, M=None, inplace=True, complex=False, device='cpu', togpu=(False, False), tocpu=(False, False), dtype=torch.float32)[source]

Identity operator.

Simply move model to data in forward model and viceversa in adjoint mode if \(M = N\). If \(M > N\) removes last \(M - N\) elements from model in forward and pads with \(0\) in adjoint. If \(N > M\) removes last \(N - M\) elements from data in adjoint and pads with \(0\) in forward.

Parameters:
N : int

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

M : int, optional

Number of samples in model.

inplace : bool, optional

Work inplace (True) or make a new copy (False). By default, data is a reference to the model (in forward) and model is a reference to the data (in adjoint).

complex : bool, optional

Input model and data are complex arrays

device : str, optional

Device to be used

togpu : tuple, optional

Move model and data from cpu to gpu prior to applying matvec and rmatvec, respectively (only when device='gpu')

tocpu : tuple, optional

Move data and model from gpu to cpu after applying matvec and rmatvec, respectively (only when device='gpu')

dtype : torch.dtype, optional

Type of elements in input array (if complex=True, provide the type of the real component of the array)

Notes

Refer to pylops.basicoperators.Identity for implementation details.

Attributes:
shape : tuple

Operator shape

explicit : bool

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

Methods

__init__(N[, M, inplace, complex, device, …]) 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, tol]) Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x) Matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg]) Most significant eigenvalues of linear operator.
matmat(X[, kfirst]) Matrix-matrix multiplication.
matvec(x) Matrix-vector multiplication.
rmatmat(X[, kfirst]) Adjoint 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.
transpose() Transpose this linear operator.

Examples using pylops_gpu.Identity