SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Variables

• useUmfpack

  useUmfpack = True
  
  

Function summary

• factorized(A)
• splu(A, permc_spec = 2, diag_pivot_thresh = 1.0, drop_tol = 0.0, relax = 1, panel_size = 10)
• spsolve(A, b, permc_spec = 2)
• use_solver(**kwargs)

Functions

• factorized(A)

  Return a fuction for solving a sparse linear system, with A pre-factorized.

  Example:

  solve = factorized( A ) # Makes LU decomposition. x1 = solve( rhs1 ) # Uses the LU factors. x2 = solve( rhs2 ) # Uses again the LU factors.

• splu(A, permc_spec = 2, diag_pivot_thresh = 1.0, drop_tol = 0.0, relax = 1, panel_size = 10)

  A linear solver, for a sparse, square matrix A, using LU decomposition where L is a lower triangular matrix and U is an upper triagular matrix.

  Returns a factored_lu object. (scipy.linsolve._superlu.SciPyLUType)

  See scipy.linsolve._superlu.dgstrf for more info.

• spsolve(A, b, permc_spec = 2)
• use_solver(**kwargs)

  Valid keyword arguments with defaults (other ignored):

  useUmfpack = True assumeSortedIndices = False

  The default sparse solver is umfpack when available. This can be changed by passing useUmfpack = False, which then causes the always present SuperLU based solver to be used.

  Umfpack requires a CSR/CSC matrix to have sorted column/row indices. If sure that the matrix fulfills this, pass assumeSortedIndices = True to gain some speed.

Imported Names