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Various literature and library implementations like petsc use preconditioners based on simple smoothers that themselves could be used the solve the systems directly. e.g. say I have a function

void do_jacobi_smooth(const matrix A, vector x, const vector b);

which updates $x$ for the system $Ax=b$. Say I have a system where repeated calls to this eventually converge.

What I'm confused by in the literature of preconditioned methods, is that e.g. from the wiki page https://en.wikipedia.org/wiki/Conjugate_gradient_method for conjugate gradient you see the use of a preconditioner $M^{-1}$ written as

$$ z_0 := M^{-1}r_0 $$

so that $M^{-1}$ is some kind of (here linear) operator.

My question is what is the form of this operator, as implemented by something like the do_jacobi_smooth(A,x,b) above? In other words, say I have an unpreconditioned conjugate gradient implementation, and a jacobi smoother implementation (or a multigrid solver), how do I connect these parts to get a preconditioned CG?

Is it as simple as applying one iteration with a 0 initial guess?

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