(self, data_instances, delta_s, forward_hess)
return np.array(hess_vector)
To compute Hessian matrix, y, s are needed.
        g = (1/N)*∑(0.25 * wx - 0.5 * y) * x
        y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(0.25 * x * s) * x
        define forward_hess = (0.25 * x * s)
To compute Hessian matrix, y, s are needed.
        g = (1/N)*∑(0.25 * wx - 0.5 * y) * x
        y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(0.25 * x * s) * x
        define forward_hess = (0.25 * x * s)
To compute Hessian matrix y s are needed . g = ( 1 / N ) * ∑ ( 0 . 25 * wx - 0 . 5 * y ) * x y = ∇2^F ( w_t ) s_t = g * s = ( 1 / N ) * ∑ ( 0 . 25 * x * s ) * x define forward_hess = ( 0 . 25 * x * s )
def compute_forward_hess(self, data_instances, delta_s, forward_hess):
        """
        To compute Hessian matrix, y, s are needed.
        g = (1/N)*∑(0.25 * wx - 0.5 * y) * x
        y = ∇2^F(w_t)s_t = g' * s = (1/N)*∑(0.25 * x * s) * x
        define forward_hess = (0.25 * x * s)
        """
        hess_vector = self.compute_gradient(data_instances,
                                       forward_hess,
                                       delta_s.fit_intercept)
        return np.array(hess_vector)
def compute_forward_hess ( self , data_instances , delta_s , forward_hess ) : hess_vector = self . compute_gradient ( data_instances , forward_hess , delta_s . fit_intercept ) return np . array ( hess_vector )