International audienceIn this article, we study the problem of enumerating the models of DNF formulas. The aim is to provide enumeration algorithms with a delay that depends polynomially on the size of each model and not on the size of the formula. We succeed for two subclasses of DNF formulas: we provide a constant delay algorithm for k-DNF with fixed k by an appropriate amortization method and we give a polynomial delay algorithm for monotone formulas. We then focus on the average delay of enumeration algorithms and show that we can bring down the dependency of the average delay to the square root of the formula size and even to a logarithmic dependency for monotone formulas
We show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctive normal...
We consider exact learning monotone CNF formulas in which each variable appears at most some consta...
In general, it is difficult to enumerate all vertices of a polytope in polynomial time. Here we pres...
International audienceIn this article, we study the problem of enumerating the models of DNF formula...
International audienceWe study the problem of enumerating the satisfying valuations of a circuit whi...
International audienceIn this paper, we introduce a technique we call geometric amortization for enu...
AbstractThis paper presents an algorithm that uses equivalence and membership queries to learn the c...
In this note we explore several problems related to enumeration complexity. In particular, we are in...
We describe a deterministic algorithm that, for constant k, given a k-DNF or k-CNF formula ϕ and a p...
We describe a deterministic algorithm that, for constant k, given a k-DNF or k-CNF formula phi and a...
International audienceWe investigate the relationship between several enumeration complexity classes...
We consider the problem of learning k-term DNF formulas using equivalence queries and incomplete mem...
Abstract. We investigate the complexity of finding prime implicants and minimal equivalent DNFs for ...
We are interested in computing $k$ most preferred models of a given d-DNNF circuit $C$, where the pr...
We investigate the complexity of finding prime implicants and minimum equiv-alent DNFs for Boolean f...
We show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctive normal...
We consider exact learning monotone CNF formulas in which each variable appears at most some consta...
In general, it is difficult to enumerate all vertices of a polytope in polynomial time. Here we pres...
International audienceIn this article, we study the problem of enumerating the models of DNF formula...
International audienceWe study the problem of enumerating the satisfying valuations of a circuit whi...
International audienceIn this paper, we introduce a technique we call geometric amortization for enu...
AbstractThis paper presents an algorithm that uses equivalence and membership queries to learn the c...
In this note we explore several problems related to enumeration complexity. In particular, we are in...
We describe a deterministic algorithm that, for constant k, given a k-DNF or k-CNF formula ϕ and a p...
We describe a deterministic algorithm that, for constant k, given a k-DNF or k-CNF formula phi and a...
International audienceWe investigate the relationship between several enumeration complexity classes...
We consider the problem of learning k-term DNF formulas using equivalence queries and incomplete mem...
Abstract. We investigate the complexity of finding prime implicants and minimal equivalent DNFs for ...
We are interested in computing $k$ most preferred models of a given d-DNNF circuit $C$, where the pr...
We investigate the complexity of finding prime implicants and minimum equiv-alent DNFs for Boolean f...
We show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctive normal...
We consider exact learning monotone CNF formulas in which each variable appears at most some consta...
In general, it is difficult to enumerate all vertices of a polytope in polynomial time. Here we pres...