deepmr.optim.cg_solve#
- deepmr.optim.cg_solve = <function cg_solve>#
Solve inverse problem using Conjugate Gradient method.
- Parameters:
input (np.ndarray | torch.Tensor) – Signal to be reconstructed. Assume it is the adjoint AH of measurement operator A applied to the measured data y (i.e., input = AHy).
AHA (Callable | torch.Tensor | np.ndarray) – Normal operator AHA = AH * A.
niter (int, optional) – Number of iterations. The default is
10
.device (str, optional) – Computational device. The default is
None
(infer from input).tol (float, optional) – Stopping condition. The default is
1e-4
.lamda (float, optional) – Tikhonov regularization strength. The default is
0.0
.ndim (int, optional) – Number of spatial dimensions of the problem. It is used to infer the batch axes. If
AHA
is adeepmr.linop.Linop
operator, this is inferred fromAHA.ndim
andndim
is ignored.
- Returns:
output – Reconstructed signal.
- Return type:
np.ndarray | torch.Tensor