scipy.linalg.
lu_solve#
- scipy.linalg.lu_solve(lu_and_piv, b, trans=0, overwrite_b=False, check_finite=True)[source]#
- Solve an equation system, a x = b, given the LU factorization of a - Parameters:
- (lu, piv)
- Factorization of the coefficient matrix a, as given by lu_factor. In particular piv are 0-indexed pivot indices. 
- barray
- Right-hand side 
- trans{0, 1, 2}, optional
- Type of system to solve: - trans - system - 0 - a x = b - 1 - a^T x = b - 2 - a^H x = b 
- overwrite_bbool, optional
- Whether to overwrite data in b (may increase performance) 
- check_finitebool, optional
- Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. 
 
- Returns:
- xarray
- Solution to the system 
 
 - See also - lu_factor
- LU factorize a matrix 
 - Examples - >>> import numpy as np >>> from scipy.linalg import lu_factor, lu_solve >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]]) >>> b = np.array([1, 1, 1, 1]) >>> lu, piv = lu_factor(A) >>> x = lu_solve((lu, piv), b) >>> np.allclose(A @ x - b, np.zeros((4,))) True