# pylops.optimization.solver.cg¶

pylops.optimization.solver.cg(Op, 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.

Parameters: 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

Notes

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