Fast approximate calculation of valid domains in a satisfiability-based product configurator

Calculating valid domains is an important feature of an interactive product configurator. Since it is an NP hard problem, it is necessary (for large real-world instances) to calculate valid domains only approximately in order to keep the response time low. In this paper, we present a new fast and accurate approximation algorithm to calculate the valid domains in a satisfiability based interactive product configurator. The algorithm is based on building a full implication graph during unit propagation and performing a search in that implication graph in order to approximate whether a domain value is valid. We experimentally compared our new algorithm to the algorithm used by the commercial SAT-based configurator CAS Merlin and measured speedups of up to 18-fold while maintaining the same accuracy

Computing Storyline Visualizations with Few Block Crossings

Storyline visualizations show the structure of a story, by depicting the interactions of the characters over time. Each character is represented by an x-monotone curve from left to right, and a meeting is represented by having the curves of the participating characters run close together for some time. There have been various approaches to drawing storyline visualizations in an automated way. In order to keep the visual complexity low, rather than minimizing pairwise crossings of curves, we count block crossings, that is, pairs of intersecting bundles of lines. Partly inspired by the ILP-based approach of Gronemann et al. [GD 2016] for minimizing the number of pairwise crossings, we model the problem as a satisfiability problem (since the straightforward ILP formulation becomes more complicated and harder to solve). Having restricted ourselves to a decision problem, we can apply powerful SAT solvers to find optimal drawings in reasonable time. We compare this SAT-based approach with two exact algorithms for block crossing minimization, using both the benchmark instances of Gronemann et al. and random instances. We show that the SAT approach is suitable for real-world instances and identify cases where the other algorithms are preferable.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Evaluating QBF Solvers: Quantifier Alternations Matter

We present an experimental study of the effects of quantifier alternations on the evaluation of quantified Boolean formula (QBF) solvers. The number of quantifier alternations in a QBF in prenex conjunctive normal form (PCNF) is directly related to the theoretical hardness of the respective QBF satisfiability problem in the polynomial hierarchy. We show empirically that the performance of solvers based on different solving paradigms substantially varies depending on the numbers of alternations in PCNFs. In related theoretical work, quantifier alternations have become the focus of understanding the strengths and weaknesses of various QBF proof systems implemented in solvers. Our results motivate the development of methods to evaluate orthogonal solving paradigms by taking quantifier alternations into account. This is necessary to showcase the broad range of existing QBF solving paradigms for practical QBF applications. Moreover, we highlight the potential of combining different approaches and QBF proof systems in solvers.Comment: preprint of a paper to be published at CP 2018, LNCS, Springer, including appendi

On the Usefulness of Clause Strengthening in Parallel SAT Solving

International audienc

On the combination of argumentation solvers into parallel portfolios

In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel portfolios of argumentation solvers for enumerating the preferred extensions of a given framework. In particular, four methodologies aim at combining solvers in static portfolios, while two methodologies are designed for the dynamic configuration of parallel portfolios. Our empirical results demonstrate that the configuration of parallel portfolios is a fruitful way for exploiting multicore machines, and that the presented approaches outperform the state of the art of parallel argumentation solver

Modular and Efficient Divide-and-Conquer SAT Solver on Top of the Painless Framework

International audienceOver the last decade, parallel SATisfiability solving has been widely studied from both theoretical and practical aspects. There are two main approaches. First, divide-and-conquer (D&C) splits the search space, each solver being in charge of a particular subspace. The second one, portfolio launches multiple solvers in parallel, and the first to find a solution ends the computation. However although D&C based approaches seem to be the natural way to work in parallel, portfolio ones experimentally provide better performances. An explanation resides on the difficulties to use the native formulation of the SAT problem (i.e., the CNF form) to compute an apriori good search space partitioning (i.e.,all parallel solvers process their sub-spaces in comparable computational time). To avoid this, dynamic load balancing of the search subspaces is implemented. Unfortunately, this isdifficult to compare load balancing strategies since state-of-the-art SAT solvers appropriately dealing with these aspects are hardly adaptable tovarious strategies than the ones they have been designed for. This paper aims at providing a way to overcome this problem by proposing an implementation and evaluation of different types of divide-and-conquer inspired from the literature. These are relying on thePainless framework, which provides concurrent facilities to elaborate such parallel SAT solvers. Comparison of the various strategies are thendiscussed

PaInleSS: a Framework for Parallel SAT Solving.

International audienceOver the last decade, parallel SAT solving has been widelystudied from both theoretical and practical aspects. There are now numeroussolvers that dier by parallelization strategies, programming languages,concurrent programming, involved libraries, etc.Hence, comparing the eciency of the theoretical approaches is a challengingtask. Moreover, the introduction of a new approach needs eithera deep understanding of the existing solvers, or to start from scratch theimplementation of a new tool.We present PaInleSS: a framework to build parallel SAT solvers formany-core environments. Thanks to its genericity and modularity, it providesthe implementation of basics for parallel SAT solving like clauseexchanges, Portfolio and Divide-and-Conquer strategies. It also enablesusers to easily create their own parallel solvers based on new strategies.Our experiments show that our framework compares well with some ofthe best state-of-the-art solvers