scipy.sparse.linalg.cg¶
-
scipy.sparse.linalg.
cg
(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None)¶ Use Conjugate Gradient iteration to solve
Ax = b
.- Parameters
- A{sparse matrix, dense matrix, LinearOperator}
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 produceAx
using, e.g.,scipy.sparse.linalg.LinearOperator
.- b{array, matrix}
Right hand side of the linear system. Has shape (N,) or (N,1).
- Returns
- x{array, matrix}
The converged solution.
- infointeger
- Provides convergence information:
0 : successful exit >0 : convergence to tolerance not achieved, number of iterations <0 : illegal input or breakdown
- Other Parameters
- x0{array, matrix}
Starting guess for the solution.
- tol, atolfloat, optional
Tolerances for convergence,
norm(residual) <= max(tol*norm(b), atol)
. The default foratol
is'legacy'
, which emulates a different legacy behavior.Warning
The default value for atol will be changed in a future release. For future compatibility, specify atol explicitly.
- maxiterinteger
Maximum number of iterations. Iteration will stop after maxiter steps even if the specified tolerance has not been achieved.
- M{sparse matrix, dense matrix, LinearOperator}
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.
- callbackfunction
User-supplied function to call after each iteration. It is called as callback(xk), where xk is the current solution vector.