Deep learning models achievetremendous successes across amultitude oftasks, yettheir training and inference often yield high computational costs and long processing times [11,22].

However, finding Lyapunov functions for general nonlinear systems is a challenging task.

In short, the visual analysis further proves the necessity and effectiveness of our hierarchical LSTT. The hierarchical matching is not simply a combination of multiple matching processes.

In addition, we need only assumptions weaker than those of existing algorithms; our algorithms work on discrete action sets as well as continuous ones without apriori knowledge about losses, and theyrun efficiently ifalinear optimization oracle for the action set is available.