Résumé | Parameterized complexity is the branch of complexity theory that seeks to identify in the computational complexity of some problems the dependence on different parameters of the input. The coverability and boundedness problems for Petri nets are known to be EXPSPACE-complete. Previous work [Ex: L.E. Rosier and H.-C. Yen. A multiparameter analysis of the boundedness problem for vector addition systems. JCSS 32, 1986] has already been done to analyse the dependency of this complexity on different parameters, such as the number of places, maximum arc weight etc. We continue this work and study the dependency of the complexity on two parameters called benefit depth and vertex cover. If k denotes a parameter and n denotes the size of the input for a problem, then the problem is said to be in the parameterized complexity class PARAPSpace if there is an algorithm to solve the problem that uses memory space O(f(k)poly(n)), where f(k) is any computable function of the parameter and poly(n) is some polynomial of the input size. We show PARAPspace results for the coverability and boundedness problems using the parameters mentioned above. We also show PARAPspace results for model checking a logic that can express some extensions of coverability and boundedness. This is joint work with Kamal Lodaya. |