(self, states, next_states)
return v_dot + v_dot_error
Compute confidence intervals for the decrease along Lyapunov function.

        Parameters
        ----------
        states : np.array
            The states at which to start (could be equal to discretization).
        next_states : np.array or tuple
            The dynamics evaluated at each point on the discretization. If
            the dynamics are uncertain then next_states is a tuple with mean
            and error bounds.

        Returns
        -------
        upper_bound : np.array
            The upper bound on the change in values at each grid point.
Compute confidence intervals for the decrease along Lyapunov function.
Compute confidence intervals for the decrease along Lyapunov function .
def v_decrease_bound(self, states, next_states):
        """Compute confidence intervals for the decrease along Lyapunov function.

        Parameters
        ----------
        states : np.array
            The states at which to start (could be equal to discretization).
        next_states : np.array or tuple
            The dynamics evaluated at each point on the discretization. If
            the dynamics are uncertain then next_states is a tuple with mean
            and error bounds.

        Returns
        -------
        upper_bound : np.array
            The upper bound on the change in values at each grid point.

        """
        v_dot, v_dot_error = self.v_decrease_confidence(states, next_states)

        return v_dot + v_dot_error
def v_decrease_bound ( self , states , next_states ) : v_dot , v_dot_error = self . v_decrease_confidence ( states , next_states ) return v_dot + v_dot_error