, y, x0, niter=10, damp=0.0, tol=0.0001, show=False, callback=None)[source]

Conjugate gradient

Solve a square system of equations given an operator Op and data y using conjugate gradient iterations.

Op : pylops.LinearOperator

Operator to invert of size \([N \times N]\)

y : np.ndarray

Data of size \([N \times 1]\)

x0 : np.ndarray, optional

Initial guess

niter : int, optional

Number of iterations

damp : float, optional

Deprecated, will be removed in v2.0.0

tol : float, optional

Tolerance on residual norm

show : bool, optional

Display iterations log

callback : callable, optional

Function with signature (callback(x)) to call after each iteration where x is the current model vector

x : np.ndarray

Estimated model of size \([N \times 1]\)

iit : int

Number of executed iterations

cost : numpy.ndarray, optional

History of the L2 norm of the residual


Solve the \(\mathbf{y} = \mathbf{Opx}\) problem using conjugate gradient iterations.