cg(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None)
Starting guess for the solution.
Tolerances for convergence, norm(residual) <= max(tol*norm(b), atol)
. The default for atol
is 'legacy'
, which emulates a different legacy behavior.
The default value for :None:None:`atol` will be changed in a future release. For future compatibility, specify :None:None:`atol` explicitly.
Maximum number of iterations. Iteration will stop after maxiter steps even if the specified tolerance has not been achieved.
Preconditioner for A. The preconditioner should approximate the inverse of A. Effective preconditioning dramatically improves the rate of convergence, which implies that fewer iterations are needed to reach a given error tolerance.
User-supplied function to call after each iteration. It is called as callback(xk), where xk is the current solution vector.
The real or complex N-by-N matrix of the linear system. A
must represent a hermitian, positive definite matrix. Alternatively, A
can be a linear operator which can produce Ax
using, e.g., scipy.sparse.linalg.LinearOperator
.
Right hand side of the linear system. Has shape (N,) or (N,1).
Use Conjugate Gradient iteration to solve Ax = b
.
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