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Josep Pon

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3 papers

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SAT Conference 2021 Conference Paper

OptiLog: A Framework for SAT-based Systems

Carlos Ansótegui
Jesus Ojeda
António Pacheco 0004
Josep Pon
Josep M. Salvia
Eduard Torres

Abstract We present OptiLog, a new Python framework for rapid prototyping of SAT-based systems. OptiLog allows to use and integrate SAT solvers currently developed in C/C++ just by implementing the iSAT C++ interface. It also provides a Python binding to the PBLib C++ toolkit for encoding Pseudo Boolean and Cardinality constraints. Finally, it leverages the power of automatic configurators by allowing to easily create configuration scenarios including multiple solvers and encoders.

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SAT Conference 2021 Conference Paper

PyDGGA: Distributed GGA for Automatic Configuration

Carlos Ansótegui
Josep Pon
Meinolf Sellmann
Kevin Tierney

Abstract We present PyDGGA, a Python tool that implements a distributed version of the automatic algorithm configurator GGA, which is a specialized genetic algorithm to find high quality parameters for solvers and algorithms. PyDGGA implements GGA using an event-driven architecture and runs a simulation of future generations of the genetic algorithm to maximize the usage of the available computing resources. Overall, PyDGGA offers a friendly interface to deploy elastic distributed AC scenarios on shared high-performance computing clusters.

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AAAI Conference 2017 Conference Paper

Reactive Dialectic Search Portfolios for MaxSAT

Carlos Anstegui
Josep Pon
Meinolf Sellmann
Kevin Tierney

Metaheuristics have been developed to provide general purpose approaches for solving hard combinatorial problems. While these frameworks often serve as the starting point for the development of problem-speciﬁc search procedures, they very rarely work efﬁciently in their default state. We combine the ideas of reactive search, which adjusts key parameters during search, and algorithm conﬁguration, which ﬁne-tunes algorithm parameters for a given set of problem instances, for the automatic compilation of a portfolio of highly reactive dialectic search heuristics for MaxSAT. Even though the dialectic search metaheuristic knows nothing more about MaxSAT than how to evaluate the cost of a truth assignment, our automatically generated solver de- ﬁnes a new state of the art for random weighted partial MaxSAT instances. Moreover, when combined with an industrial MaxSAT solver, the self-assembled reactive portfolio was able to win four out of nine gold medals at the recent 2016 MaxSAT Evaluation on random, crafted, and industrial partial and weighted-partial MaxSAT instances.

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