António Morgado

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Several MaxSAT algorithms based on iterative SAT solving have been proposed in recent years. These algorithms are in general the most efficient for real-world applications. Existing data indicates that, among MaxSAT algorithms based on iterative SAT solving, the most efficient ones are core-guided, i.e. algorithms which guide the search by iteratively(More)
Maximum Satisfiability (MaxSAT) is an optimization version of SAT, and many real world applications can be naturally encoded as such. Solving MaxSAT is an important problem from both a theoretical and a practical point of view. In recent years, there has been considerable interest in developing efficient algorithms and several families of algorithms have(More)
Maximum Satisfiability (MaxSAT) and its weighted variants are wellknown optimization formulations of Boolean Satisfiability (SAT). Motivated by practical applications, recent years have seen the development of core-guided algorithms for MaxSAT. Among these, core-guided binary search with disjoint cores (BCD) represents a recent robust solution. This paper(More)
Minimum Satisfiability (MinSAT) denotes one of the optimization versions of the Boolean Satisfiability (SAT) problem. In some settings MinSAT is preferred to using Maximum Satis-fiability (MaxSAT). Several encodings and dedicated branch and bound algorithms for MinSAT have been recently proposed, and evaluated on small challenging randomly generated(More)
Enumeration of Minimal Correction Sets (MCS) finds a wide range of practical applications, including the identification of Minimal Unsatisfiable Subsets (MUS) used in verifying the complex control logic of microprocessor designs (e.g. in the CEGAR loop of Reveal [1,2]). Current state of the art MCS enumeration exploits core-guided MaxSAT algorithms, namely(More)
A large number of practical applications rely on effective algorithms for propositional model enumeration and counting. Examples include knowledge compilation, model checking and hybrid solvers. Besides practical applications, the problem of counting propositional models is of key relevancy in computational complexity. In recent years a number of algorithms(More)
This paper addresses the problem of counting models in integer linear programming (ILP) using Boolean Satisfiability (SAT) techniques, and proposes two approaches to solve this problem. The first approach consists of encoding ILP instances into pseudo-Boolean (PB) instances. Moreover, the paper introduces a model counter for PB constraints, which can be(More)