(self)
return self._cholesky()
Returns the Cholesky decomposition L of a matrix A
        such that L * L.T = A

        A must be a square, symmetric, positive-definite
        and non-singular matrix.

        Examples
        ========

        >>> from sympy.matrices import Matrix
        >>> A = Matrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
        >>> A.cholesky()
        Matrix([
        [ 5, 0, 0],
        [ 3, 3, 0],
        [-1, 1, 3]])
        >>> A.cholesky() * A.cholesky().T
        Matrix([
        [25, 15, -5],
        [15, 18,  0],
        [-5,  0, 11]])

        See Also
        ========

        LDLdecomposition
        LUdecomposition
        QRdecomposition
Returns the Cholesky decomposition L of a matrix A
        such that L * L.T = A
Returns the Cholesky decomposition L of a matrix A such that L * L . T = A
def cholesky(self):
        """Returns the Cholesky decomposition L of a matrix A
        such that L * L.T = A

        A must be a square, symmetric, positive-definite
        and non-singular matrix.

        Examples
        ========

        >>> from sympy.matrices import Matrix
        >>> A = Matrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))
        >>> A.cholesky()
        Matrix([
        [ 5, 0, 0],
        [ 3, 3, 0],
        [-1, 1, 3]])
        >>> A.cholesky() * A.cholesky().T
        Matrix([
        [25, 15, -5],
        [15, 18,  0],
        [-5,  0, 11]])

        See Also
        ========

        LDLdecomposition
        LUdecomposition
        QRdecomposition
        """

        if not self.is_square:
            raise NonSquareMatrixError("Matrix must be square.")
        if not self.is_symmetric():
            raise ValueError("Matrix must be symmetric.")
        return self._cholesky()
def cholesky ( self ) : if not self . is_square : raise NonSquareMatrixError ( "Matrix must be square." ) if not self . is_symmetric ( ) : raise ValueError ( "Matrix must be symmetric." ) return self . _cholesky ( )