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bundles / scipy latest / scipy / optimize / _linprog_ip / _get_solver

function

scipy.optimize._linprog_ip:_get_solver

source: /scipy/optimize/_linprog_ip.py :44

Signature

def   _get_solver ( M sparse = False lstsq = False sym_pos = True cholesky = True permc_spec = MMD_AT_PLUS_A )

Summary

Given solver options, return a handle to the appropriate linear system solver.

Parameters

M : 2-D array

As defined in [4] Equation 8.31

sparse : bool (default = False)

True if the system to be solved is sparse. This is typically set True when the original A_ub and A_eq arrays are sparse.

lstsq : bool (default = False)

True if the system is ill-conditioned and/or (nearly) singular and thus a more robust least-squares solver is desired. This is sometimes needed as the solution is approached.

sym_pos : bool (default = True)

True if the system matrix is symmetric positive definite Sometimes this needs to be set false as the solution is approached, even when the system should be symmetric positive definite, due to numerical difficulties.

cholesky : bool (default = True)

True if the system is to be solved by Cholesky, rather than LU, decomposition. This is typically faster unless the problem is very small or prone to numerical difficulties.

permc_spec : str (default = 'MMD_AT_PLUS_A')

Sparsity preservation strategy used by SuperLU. Acceptable values are:

  • NATURAL: natural ordering.

  • MMD_ATA: minimum degree ordering on the structure of A^T A.

  • MMD_AT_PLUS_A: minimum degree ordering on the structure of A^T+A.

  • COLAMD: approximate minimum degree column ordering.

See SuperLU documentation.

Returns

solve : function

Handle to the appropriate solver function

Aliases

  • scipy.optimize._linprog_ip._get_solver