Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Fundamenta Informaticae

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Restoring CSP Satisfiability with MaxSAT

Lynce I., Marques-Silva J.

Fundamenta Informaticae

2011

Vol. 107, nr 2/3

249-266

The extraction of a Minimal Unsatisfiable Core (MUC) in a Constraint Satisfaction Problem (CSP) aims to identify a subset of constraints that make a CSP instance unsatisfiable. Recent work has addressed the identification of a Minimal Set of Unsatisfiable Tuples (MUST) in order to restore the CSP satisfiability with respect to that MUC. A two-step algorithm has been proposed: first, a MUC is identified, and second, a MUST in the MUC is identified. This paper proposes an integrated algorithm for restoring satisfiability in a CSP, making use of a MaxSAT solver. The proposed approach encodes the CSP instance as a partial MaxSAT instance, in such a way that solving the MaxSAT instance corresponds to identifying the smallest set of tuples to be removed from the CSP instance to restore satisfiability. Experimental results illustrate the feasibility of the approach.

Combinatorial Optimization Solutions for the Maximum Quartet Consistency Problem

Morgado A., Marques-Silva J.

Fundamenta Informaticae

2010

Vol. 102, nr 3/4

363-389

Phylogenetic analysis is a widely used technique, for example in biology and biomedical sciences. The construction of phylogenies can be computationally hard. A commonly used solution for construction of phylogenies is to start from a set of biological species and relations among those species. This work addresses the case where the relations among species are specified as quartet topologies. Moreover, the problem to be solved consists of computing a phylogeny that satisfies the maximum number of quartet topologies. This is referred to as the Maximum Quartet Consistency (MQC) problem, and represents an NP-hard optimization problem. MQC has been solved both heuristically and exactly. Exact solutions forMQC include those based on Constraint Programming, Answer Set Programming, Pseudo-Boolean Optimization (PBO), and SatisfiabilityModulo Theories (SMT). This paper provides a comprehensive overview of the use of PBO and SMT for solving MQC, and builds on recent work in this area. Moreover, the paper provides new insights on how to use SMT for solving optimization problems, by focusing on the concrete case of MQC. The solutions based on PBO and SMT were experimentally compared with other exact solutions. The results show that for instances with small percentage of quartet errors, the models based on SMT can be competitive, whereas for instances with higher number of quartet errors the PBO models are more efficient.