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Inês Lynce
Researcher at Instituto Superior Técnico
Publications - 124
Citations - 3131
Inês Lynce is an academic researcher from Instituto Superior Técnico. The author has contributed to research in topics: Boolean satisfiability problem & Maximum satisfiability problem. The author has an hindex of 27, co-authored 121 publications receiving 2861 citations. Previous affiliations of Inês Lynce include Technical University of Lisbon & University of Lisbon.
Papers
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Conflict-Driven clause learning SAT solvers
TL;DR: Sophisticated power management features are implemented by the present invention independent from operating system control, and the operating system need not suspend processing of other threads to process sophisticated power management procedures.
Book ChapterDOI
Open-WBO: A Modular MaxSAT Solver ,
TL;DR: This paper presents open-wbo, a new MaxSAT solver, an open-source solver that can be easily modified and extended that may use any MiniSAT-like solver as the underlying SAT solver.
Proceedings Article
On Computing Minimum Unsatisfiable Cores
Inês Lynce,Joao Marques-Silva +1 more
TL;DR: As part of the process of generating unsatisfiable proofs, one is also interested in unsatisfiable subformulas of the original formula, also known as unsatisfiable cores, which may be very useful in AI problems where identifying the minimum core is crucial for correcting the minimum amount of inconsistent information.
Sudoku as a SAT Problem.
Inês Lynce,Joël Ouaknine +1 more
TL;DR: This paper addresses the problem of encoding Sudoku puzzles into conjunctive normal form (CNF), and subsequently solving them using polynomial-time propositional satisfiability (SAT) inference techniques, and introduces two straightforward SAT encodings for Sudoku: the minimal encoding and the extended encoding.
Book ChapterDOI
Towards robust CNF encodings of cardinality constraints
Joao Marques-Silva,Inês Lynce +1 more
TL;DR: Experimental results indicate that the modified SAT solver becomes significantly more robust on SAT encodings involving ≤ 1 (x1, . . . , xn) constraints, and shows how a state-of-the-art SAT solvers can be adapted to overcome the problem of adding additional auxiliary variables.