Ana Graça
Technical University of Lisbon
10 Papers
54 Citations
Ana Graça is an academic researcher from Technical University of Lisbon. The author has contributed to research in topics: Boolean satisfiability problem & Integer programming. The author has an hindex of 7, co-authored 10 publications. Previous affiliations of Ana Graça include The Catholic University of America.
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Papers
Boolean lexicographic optimization: algorithms & applications
TL;DR: This paper develops and evaluates algorithms for solving MOCO problems, defined on Boolean domains, and where the optimality criterion is lexicographic, and shows that lexicography optimization conditions are observed in the majority of the problem instances from the MaxSAT evaluations.
Efficient haplotype inference with pseudo-boolean optimization
Ana Graça,Joao Marques-Silva,Inês Lynce,Arlindo L. Oliveira +3 more
- 02 Jul 2007
TL;DR: Experimental results indicate that RPoly outperforms the SAT-based approach on most problem instances, being, in general, significantly more efficient.
Efficient and accurate haplotype inference by combining parsimony and pedigree information
Ana Graça,Inês Lynce,Joao Marques-Silva,Arlindo L. Oliveira +3 more
- 31 Jul 2010
TL;DR: A new Boolean optimization model for haplotype inference combining two combinatorial approaches: the Minimum Recombinant Haplotyping Configuration (MRHC), which minimizes the number of recombinant events within a pedigree, and the Haplotype Inference by Pure Parsimony (HIPP), that aims at finding a solution with a minimum number of distinct haplotypes within a population.
Haplotype inference with pseudo-Boolean optimization
TL;DR: This paper provides a detailed description of RPoly, a PBO approach for the haplotype inference by pure parsimony (HIPP) problem and an extensive evaluation of existent HIPP solvers confirms that RPoly is currently the most efficient and robust HIPP approach.
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Haplotype inference by Pure Parsimony: a survey.
TL;DR: HIPP can now be regarded as a feasible approach for haplotype inference, which can be competitive with other different approaches, including preprocessing, bounding techniques, and heuristic approaches.
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