conjugate_gradient¶

odl.solvers.iterative.iterative.conjugate_gradient(op, x, rhs, niter, callback=None)[source]¶

Optimized implementation of CG for self-adjoint operators.

This method solves the inverse problem (of the first kind):

A(x) = y

for a linear and self-adjoint Operator A.

It uses a minimum amount of memory copies by applying re-usable temporaries and in-place evaluation.

The method is described (for linear systems) in a Wikipedia article.

Parameters

oplinear Operator: Operator in the inverse problem. It must be linear and self-adjoint. This implies in particular that its domain and range are equal.
xop.domain element: Element to which the result is written. Its initial value is used as starting point of the iteration, and its values are updated in each iteration step.
rhsop.range element: Right-hand side of the equation defining the inverse problem.
niterint: Number of iterations.
callbackcallable, optional: Object executing code per iteration, e.g. plotting each iterate.