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Jordi Coll

6 accepted papers

2025

Improving the Lower Bound in Branch-and-Bound Algorithms for MaxSAT

AAAI 2025technical

The MaxSAT problem is an optimization version of the satisfiability problem (SAT). A tight lower bound (LB) on the number of falsified soft clauses in a MaxSAT solution is crucial for the efficiency of Branch-and-Bound (BnB) MaxSAT solvers. To compute an LB, modern BnB solvers detect disjoint incons…

Cited by 0SourcePDFScholar
2023

A New Variable Ordering for In-processing Bounded Variable Elimination in SAT Solvers

IJCAI 2023poster

Bounded Variable Elimination (BVE) is an important Boolean formula simplification technique in which the variable ordering is crucial. We define a new variable ordering based on variable activity, called ESA (variable Elimination Scheduled by Activity), for in-processing BVE in Conflict-Driven Claus…

Cited by 2SourcePDFScholar
2023

Constraint Solving Approaches to the Business-to-Business Meeting Scheduling Problem (Extended Abstract)

IJCAI 2023poster

The B2B Meeting Scheduling Optimization Problem (B2BSP) consists of scheduling a set of meetings between given pairs of participants to an event, minimizing idle time periods in participants' schedules, while taking into account participants’ availability and accommodation capacity. Therefore, it co…

Cited by 0SourcePDFScholar
2023

SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One Constraints (Extended Abstract)

IJCAI 2023poster

When solving a combinatorial problem using propositional satisfiability (SAT), the encoding of the constraints is of vital importance. Pseudo-Boolean (PB) constraints appear frequently in a wide variety of problems. When PB constraints occur together with at-most-one (AMO) constraints over the s…

Cited by 0SourcePDFScholar
2022

Combining Clause Learning and Branch and Bound for MaxSAT (Extended Abstract)

IJCAI 2022poster

Branch and Bound (BnB) has been successfully used to solve many combinatorial optimization problems. However, BnB MaxSAT solvers perform poorly when solving real-world and academic optimization problems. They are only competitive for random and some crafted instances. Thus, it is a prevailing opinio…

Cited by 0SourcePDFScholar