pylops.optimization.cls_basic.CG#

class pylops.optimization.cls_basic.CG(Op, callbacks=None)[source]#

Conjugate gradient

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

Parameters
Oppylops.LinearOperator

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

Notes

Solve the \(\mathbf{y} = \mathbf{Op}\,\mathbf{x}\) problem using conjugate gradient iterations [1].

1

Hestenes, M R., Stiefel, E., “Methods of Conjugate Gradients for Solving Linear Systems”, Journal of Research of the National Bureau of Standards. vol. 49. 1952.

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, tol, show])

Setup solver

solve(y[, x0, niter, tol, show, itershow])

Run entire solver

step(x[, show])

Run one step of solver
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