scholarly journals TCTL Model Checking Lower/Upper-Bound Parametric Timed Automata Without Invariants

2018 ◽  
pp. 37-52 ◽  
Author(s):  
Étienne André ◽  
Didier Lime ◽  
Mathias Ramparison
Keyword(s):  
Model Checking ◽  
Upper Bound ◽  
Timed Automata ◽  
Lower Upper

Decision problems for lower/upper bound parametric timed automata

2009 ◽  
Vol 35 (2) ◽  
pp. 121-151 ◽  
Author(s):  
Laura Bozzelli ◽  
Salvatore La Torre
Keyword(s):  
Upper Bound ◽  
Timed Automata ◽  
Decision Problems ◽  
Lower Upper

scholarly journals Fast and accurate exercise policies for Bermudan swaptions in the LIBOR market model

2016 ◽  
Vol 03 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Patrik Karlsson ◽  
Shashi Jain ◽  
Cornelis W. Oosterlee
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Simulations ◽  
Upper Bound ◽  
Market Model ◽  
Monte Carlo Approach ◽  
Lower Upper ◽  
Libor Market Model ◽  
Bermudan Swaptions

This paper describes an American Monte Carlo approach for obtaining fast and accurate exercise policies for pricing of callable LIBOR Exotics (e.g., Bermudan swaptions) in the LIBOR market model using the Stochastic Grid Bundling Method (SGBM). SGBM is a bundling and regression based Monte Carlo method where the continuation value is projected onto a space where the distribution is known. We also demonstrate an algorithm to obtain accurate and tight lower–upper bound values without the need for nested Monte Carlo simulations.

Schedulability Analysis for Timed Automata With Tasks

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Jinghao Sun ◽  
Nan Guan ◽  
Rongxiao Shi ◽  
Guozhen Tan ◽  
Wang Yi
Keyword(s):  
Model Checking ◽  
State Space ◽  
Real Time ◽  
Timed Automata ◽  
Schedulability Analysis ◽  
Scheduling Theory ◽  
Real Time Scheduling ◽  
Analysis Techniques ◽  
Model Complex ◽  
Time Scheduling

Research on modeling and analysis of real-time computing systems has been done in two areas, model checking and real-time scheduling theory. In model checking, an expressive modeling formalism such as timed automata (TA) is used to model complex systems, but the analysis is typically very expensive due to state-space explosion. In real-time scheduling theory, the analysis techniques are highly efficient, but the models are often restrictive. In this paper, we aim to exploit the possibility of applying efficient analysis techniques rooted in real-time scheduling theory to analysis of real-time task systems modeled by timed automata with tasks (TAT). More specifically, we develop efficient techniques to analyze the feasibility of TAT-based task models (i.e., whether all tasks can meet their deadlines on single-processor) using demand bound functions (DBF), a widely used workload abstraction in real-time scheduling theory. Our proposed analysis method has a pseudo-polynomial time complexity if the number of clocks used to model each task is bounded by a constant, which is much lower than the exponential complexity of the traditional model-checking based analysis approach (also assuming the number of clocks is bounded by a constant). We apply dynamic programming techniques to implement the DBF-based analysis framework, and propose state space pruning techniques to accelerate the analysis process. Experimental results show that our DBF-based method can analyze a TAT system with 50 tasks within a few minutes, which significantly outperforms the state-of-the-art TAT-based schedulability analysis tool TIMES.

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