(A)
return V
:param A: Hermitian positive-definite matrix of type List[List[float]]
    :return: matrix of type List[List[float]] if A can be decomposed, otherwise None
:param A: Hermitian positive-definite matrix of type List[List[float]]
    :return: matrix of type List[List[float]] if A can be decomposed, otherwise None
: param A : Hermitian positive - definite matrix of type List [ List [ float ]] : return : matrix of type List [ List [ float ]] if A can be decomposed otherwise None
def cholesky_decomposition(A):
    """
    :param A: Hermitian positive-definite matrix of type List[List[float]]
    :return: matrix of type List[List[float]] if A can be decomposed, otherwise None
    """
    n = len(A)
    for ai in A:
        if len(ai) != n:
            return None
    V = [[0.0] * n for _ in range(n)]
    for j in range(n):
        sum_diagonal_element = 0
        for k in range(j):
            sum_diagonal_element = sum_diagonal_element + math.pow(V[j][k], 2)
        sum_diagonal_element = A[j][j] - sum_diagonal_element
        if sum_diagonal_element <= 0:
            return None
        V[j][j] = math.pow(sum_diagonal_element, 0.5)
        for i in range(j+1, n):
            sum_other_element = 0
            for k in range(j):
                sum_other_element += V[i][k]*V[j][k]
            V[i][j] = (A[i][j] - sum_other_element)/V[j][j]
    return V
def cholesky_decomposition ( A ) : n = len ( A ) for ai in A : if len ( ai ) != n : return None V = [ [ 0.0 ] * n for _ in range ( n ) ] for j in range ( n ) : sum_diagonal_element = 0 for k in range ( j ) : sum_diagonal_element = sum_diagonal_element + math . pow ( V [ j ] [ k ] , 2 ) sum_diagonal_element = A [ j ] [ j ] - sum_diagonal_element if sum_diagonal_element <= 0 : return None V [ j ] [ j ] = math . pow ( sum_diagonal_element , 0.5 ) for i in range ( j + 1 , n ) : sum_other_element = 0 for k in range ( j ) : sum_other_element += V [ i ] [ k ] * V [ j ] [ k ] V [ i ] [ j ] = ( A [ i ] [ j ] - sum_other_element ) / V [ j ] [ j ] return V