Theory of linear and integer programming
Theory of linear and integer programming
A new approach to the maximum-flow problem
Journal of the ACM (JACM)
A fast parametric maximum flow algorithm and applications
SIAM Journal on Computing
Bisimulation through probabilistic testing
Information and Computation
Deciding bisimilarity and similarity for probabilistic processes
Journal of Computer and System Sciences
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Probabilistic simulations for probabilistic processes
Nordic Journal of Computing
Decision Algorithms for Probabilistic Bisimulation
CONCUR '02 Proceedings of the 13th International Conference on Concurrency Theory
Convex Optimization
Theoretical Computer Science - Tools and algorithms for the construction and analysis of systems (TACAS 2004)
Comparative branching-time semantics for Markov chains
Information and Computation
Flow faster: efficient decision algorithms for probabilistic simulations
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
A characterization of meaningful schedulers for continuous-time markov decision processes
FORMATS'06 Proceedings of the 4th international conference on Formal Modeling and Analysis of Timed Systems
An Experimental Evaluation of Probabilistic Simulation
FORTE '08 Proceedings of the 28th IFIP WG 6.1 international conference on Formal Techniques for Networked and Distributed Systems
A Space-Efficient Probabilistic Simulation Algorithm
CONCUR '08 Proceedings of the 19th international conference on Concurrency Theory
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Probabilistic automata are a central model for concurrent systems exhibiting random phenomena. This paper presents, in a uniform setting, efficient decision algorithms for strong simulation on probabilistic automata, but with subtly different results. The algorithm for strong probabilistic simulation is shown to be of polynomial complexity via a reduction to LP problem, while the algorithm for strong simulation has complexity O(m2n). The former relation allows for convex combinations of transitions in the definition and is thus less discriminative than the latter. As a byproduct, we obtain minimisation algorithms with respect to strong simulation equivalences and - for Markov decision processes - also to strong bisimulation equivalences. When extending these algorithms to the continuous-time setting, we retain same complexities for both strong simulation and strong probabilistic simulations.