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Symbolic math toolbox state space to transfer
Symbolic math toolbox state space to transfer













symbolic math toolbox state space to transfer

Elsevier, Amsterdamīloem R, Ravi K, Somenzi F (2000) Symbolic guided search for CTL model checking. IEEE Trans Reliabil 30:123–132īergstra JA, Ponse A, Smolka SA (2001) Handbook of process algebra. Wiley, New YorkĪmoia V, De Micheli G, Santomauro M (1981) Computer-oriented formulation of transition-rate matrices via Kronecker algebra.

symbolic math toolbox state space to transfer

Saturation, in particular, is shown to be many orders of magnitude more efficient in terms of memory and time with respect to traditional methods.Ījmone Marsan M, Balbo G, Conte G, Donatelli S, Franceschinis G (1995) Modelling with generalized stochastic Petri nets. This allows us to run fair and detailed comparisons between them on a suite of representative models. The resulting algorithm merges “on-the-fly” explicit state-space generation of each submodel with symbolic state-space generation of the overall model.Įach algorithm we present is implemented in our tool SmArT. In particular, we focus on the saturation strategy, which is completely different from traditional breadth-first symbolic approaches, and extend its applicability to models where the possible values of the state variables are not known a priori. In turn, locality information suggests better iteration strategies aimed at minimizing peak memory consumption. The Kronecker encoding allows us to recognize and exploit the “locality of effect” that events might have on state variables. We present various algorithms for generating the state space of an asynchronous system based on the use of multiway decision diagrams to encode sets and Kronecker operators on boolean matrices to encode the next-state function.















Symbolic math toolbox state space to transfer