cg

mrinufft.extras.optim.cg(operator, kspace_data, damp=0.0, x0=None, x_init=None, max_iter=10, tol=0.0001, progressbar=True, callback=None)

Perform conjugate gradient (CG) optimization for image reconstruction.

The image is updated using the gradient of a data consistency term, and a velocity vector is used to accelerate convergence.

Parameters:

• $base_params

• tol (float) – Tolerance for converge check.

• $returns

• operator (FourierOperatorBase)

• kspace_data (ndarray[tuple[int, ...], dtype[_ScalarType_co]])

• damp (float)

• x0 (ndarray[tuple[int, ...], dtype[_ScalarType_co]] | None)

• x_init (ndarray[tuple[int, ...], dtype[_ScalarType_co]] | None)

• max_iter (int)

• progressbar (bool)

• callback (Callable | None)

References

https://en.m.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method

Note

This function uses numpy for all CPU arrays, and cupy for all on-gpu array. It will convert all its array argument to the respective array library. The outputs will be converted back to the original array module and device.