Jonathan strahl2e

Scipy does not currently provide a routine for cholesky decomposition of a sparse matrix, and one have to rely on another external package such as scikit.sparse for the purpose. Here I implement cholesky decomposition of a sparse matrix only using scipy functions. Our implementation relies on sparse LU deconposition.

The following function receives a sparse symmetric positive-definite matrix A and returns a spase lower triangular matrix L such that A = LL^T.

from scipy.sparse import linalg as splinalg
import scipy.sparse as sparse
import sys

def sparse_cholesky(A): # The input matrix A must be a sparse symmetric positive-definite.

Multiple SSH Keys settings for different github account

create different public key

create different ssh key according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "[email protected]"

	from uuid import UUID

	def validate_uuid4(uuid_string):

	"""
	Validate that a UUID string is in
	fact a valid uuid4.

	Happily, the uuid module does the actual
	checking for us.