(self, A, Ainv, p, alpha, beta)
not yet implemented
not yet implemented
not yet implemented
def _updateCholesky(self, A, Ainv, p, alpha, beta):
        """not yet implemented"""
        # BD is A, p is A*Normal(0,I) distributed
        # input is assumed to be numpy arrays
        # Ainv is needed to compute the evolution path
        # this is a stump and is not tested

        raise _Error("not yet implemented")
        # prepare
        alpha = float(alpha)
        beta = float(beta)
        y = np.dot(Ainv, p)
        y_sum = sum(y**2)

        # compute scalars
        tmp = sqrt(1 + beta * y_sum / alpha)
        fac = (sqrt(alpha) / sum(y**2)) * (tmp - 1)
        facinv = (1. / (sqrt(alpha) * sum(y**2))) * (1 - 1. / tmp)

        # update matrices
        A *= sqrt(alpha)
        A += np.outer(fac * p, y)
        Ainv /= sqrt(alpha)
        Ainv -= np.outer(facinv * y, np.dot(y.T, Ainv))
def _updateCholesky ( self , A , Ainv , p , alpha , beta ) : # BD is A, p is A*Normal(0,I) distributed # input is assumed to be numpy arrays # Ainv is needed to compute the evolution path # this is a stump and is not tested raise _Error ( "not yet implemented" ) # prepare alpha = float ( alpha ) beta = float ( beta ) y = np . dot ( Ainv , p ) y_sum = sum ( y ** 2 ) # compute scalars tmp = sqrt ( 1 + beta * y_sum / alpha ) fac = ( sqrt ( alpha ) / sum ( y ** 2 ) ) * ( tmp - 1 ) facinv = ( 1. / ( sqrt ( alpha ) * sum ( y ** 2 ) ) ) * ( 1 - 1. / tmp ) # update matrices A *= sqrt ( alpha ) A += np . outer ( fac * p , y ) Ainv /= sqrt ( alpha ) Ainv -= np . outer ( facinv * y , np . dot ( y . T , Ainv ) )