pylops.optimization.basic.cg¶
-
pylops.optimization.basic.
cg
(Op, y, x0=None, niter=10, tol=0.0001, show=False, itershow=[10, 10, 10], callback=None)[source]¶ Conjugate gradient
Solve a square system of equations given an operator
Op
and datay
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
- tol :
float
, optional Tolerance on residual norm
- show :
bool
, optional Display iterations log
- itershow :
list
, optional Display set log for the first N1 steps, last N2 steps, and every N3 steps in between where N1, N2, N3 are the three element of the list.
- callback :
callable
, optional Function with signature (
callback(x)
) to call after each iteration wherex
is the current model vector
Returns: - 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
- Op :