scipy.sparse.linalg.bicg#
- scipy.sparse.linalg.bicg(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None)[source]#
Use BIConjugate Gradient iteration to solve
Ax = b
.- Parameters
- A{sparse matrix, ndarray, LinearOperator}
The real or complex N-by-N matrix of the linear system. Alternatively,
A
can be a linear operator which can produceAx
andA^T x
using, e.g.,scipy.sparse.linalg.LinearOperator
.- bndarray
Right hand side of the linear system. Has shape (N,) or (N,1).
- Returns
- xndarray
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
- x0ndarray
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, ndarray, 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.
Examples
>>> from scipy.sparse import csc_matrix >>> from scipy.sparse.linalg import bicg >>> A = csc_matrix([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=float) >>> b = np.array([2, 4, -1], dtype=float) >>> x, exitCode = bicg(A, b) >>> print(exitCode) # 0 indicates successful convergence 0 >>> np.allclose(A.dot(x), b) True