(ctx, A, norm=None)
return norm(A) * norm(ctx.inverse(A))
Calculate the condition number of a matrix using a specified matrix norm.

        The condition number estimates the sensitivity of a matrix to errors.
        Example: small input errors for ill-conditioned coefficient matrices
        alter the solution of the system dramatically.

        For ill-conditioned matrices it's recommended to use qr_solve() instead
        of lu_solve(). This does not help with input errors however, it just avoids
        to add additional errors.

        Definition:    cond(A) = ||A|| * ||A**-1||
Calculate the condition number of a matrix using a specified matrix norm.
Calculate the condition number of a matrix using a specified matrix norm .
def cond(ctx, A, norm=None):
        """
        Calculate the condition number of a matrix using a specified matrix norm.

        The condition number estimates the sensitivity of a matrix to errors.
        Example: small input errors for ill-conditioned coefficient matrices
        alter the solution of the system dramatically.

        For ill-conditioned matrices it's recommended to use qr_solve() instead
        of lu_solve(). This does not help with input errors however, it just avoids
        to add additional errors.

        Definition:    cond(A) = ||A|| * ||A**-1||
        """
        if norm is None:
            norm = lambda x: ctx.mnorm(x,1)
        return norm(A) * norm(ctx.inverse(A))
def cond ( ctx , A , norm = None ) : if norm is None : norm = lambda x : ctx . mnorm ( x , 1 ) return norm ( A ) * norm ( ctx . inverse ( A ) )