pylops.optimization.cls_basic.CGLS

class pylops.optimization.cls_basic.CGLS(Op, callbacks=None)

Conjugate gradient least squares

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

Parameters

Oppylops.LinearOperator

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

Notes

Minimize the following functional using conjugate gradient iterations:

\[J = || \mathbf{y} - \mathbf{Op}\,\mathbf{x} ||_2^2 + \epsilon^2 || \mathbf{x} ||_2^2\]

where \(\epsilon\) is the damping coefficient.

Methods

__init__(Op[, callbacks])
callback(x, *args, **kwargs)	Callback routine
finalize([show])	Finalize solver
run(x[, niter, show, itershow])	Run solver
setup(y[, x0, niter, damp, tol, show])	Setup solver
solve(y[, x0, niter, damp, tol, show, itershow])	Run entire solver
step(x[, show])	Run one step of solver