The problem of determining the coarsest partition stable with respect to a given binary relation, is known to be equivalent to the problem of finding the maximal bisimulation on a given structure. Such an equivalence has suggested efficient algorithms for the computation of the maximal bisimulation relation. In this paper the simulation problem is rewritten in terms of coarsest stable partition problem allowing a more algebraic understanding of the simulation equivalence. On this ground, a new algorithm for deciding simulation is proposed. Such a procedure improves on either space or time complexity of previous simulation algorithms.

In this paper we present a study of the problem of handling constraints made by conjunctions of positive and negative literals based on the predicate symbols =, ∈,∪ and || (i.e., disjointness of two sets) in a (hybrid) universe of finite sets . We also review and compare the main techniques considered to represent finite sets in the context of logic languages. The resulting contraint algorithms are embedded in a Constraint Logic Programming (CLP) language which provides finite sets—along with basic set-theoretic operations—as first-class objects of the language. The language—called CLP( SET )—is an instance of the general CLP framework, and as such it inherits all the general features and theoretical results of this scheme. We provide, through programming examples, a taste of the expressive power offered by programming in CLP( SET ).

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In this paper we propose an efficient algorithmic solution to the problem of determining a Bisimulation Relation on a finite structure. Starting from a set-theoretic point of view we propose an algorithm that optimizes the solution to the Relational coarsest Partition problem given by Paige and Tarjan in 1987 and its use in model-checking packages is briefly discussed and tested. Our algorithm reaches, in particular cases, a linear solution.

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This paper is concerned with the problem of computing the bounded time reachable set of a polynomial discrete-time dynamical system. The problem is well-known for being difficult when nonlinear systems are considered. In this regard, we propose three reachability methods that differ in the set representation. The proposed algorithms adopt boxes, parallelotopes, and parallelotope bundles to construct flowpipes that contain the actual reachable sets. The latter is a new data structure for the symbolic representation of polytopes. Our methods exploit the Bernstein expansion of polynomials to bound the images of sets. The scalability and precision of the presented methods are analyzed on a number of dynamical systems, in comparison with other existing approaches