LMHS: A SAT-IP Hybrid MaxSAT Solver

Saikko, Paul; Berg, Jeremias; Järvisalo, Matti

doi:10.1007/978-3-319-40970-2_34

Cited by 42 publications

(39 citation statements)

References 15 publications

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“…While SAT4J [8] uses specialized data structures for PB constraints to avoid their conversion to CNF, other solvers such as QMaxSAT [18] convert the PB constraint into clauses using PB encodings [27,17,12]. Some MaxSAT solvers which are based on the implicit hitting set approach [11,25] maintain a lower and an upper bound on the values of the solution. These solvers can also be used for incomplete MaxSAT since they are also able to find intermediate solutions.…”

Section: Related Workmentioning

confidence: 99%

Approximation Strategies for Incomplete MaxSAT

Joshi

Kumar

Martins

et al. 2018

Lecture Notes in Computer Science

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Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees. In this paper, we propose two approximation strategies for improving incomplete MaxSAT solving. In one of the strategies, we cluster the weights and approximate them with a representative weight. In another strategy, we break up the problem of minimizing the sum of weights of unsatisfiable clauses into multiple minimization subproblems. Experimental results show that approximation strategies can be used to find better solutions than the best incomplete solvers in the MaxSAT Evaluation 2017.

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Section: Related Workmentioning

confidence: 99%

Approximation Strategies for Incomplete MaxSAT

Joshi

Kumar

Martins

et al. 2018

Lecture Notes in Computer Science

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“…This enables a comparison of the runtime performance of ’s default branch-and-bound based search for an optimal solution to those of other MaxSAT solvers implementing alternative algorithmic approaches on the direct propositional MaxSAT encoding. Results comparing the performance of to that of the modern MaxSAT solvers Eva500a (Narodytska and Bacchus, 2014), LMHS (Saikko et al, 2016), MSCG (Morgado et al, 2015), Open-WBO (Martins et al, 2014), PrimalDual (Bjørner and Narodytska, 2015), and QMaxSAT (Koshimura et al, 2012), as well as the commercial integer programming (IP) solver CPLEX run on a standard IP translation of MaxSAT (Davies and Bacchus, 2013; Ansótegui and Gabàs, 2013), are shown in Figure 13. Here we observe that ’s branch-and-bound approach is among the best performing solvers (with the considered problem parameters).…”

Section: Solver Performance Comparisonmentioning

confidence: 99%

A constraint optimization approach to causal discovery from subsampled time series data

Hyttinen

Plis

Järvisalo

et al. 2017

International Journal of Approximate Reasoning

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We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system’s causal structure if not properly taken into account. In this paper, we first consider the search for system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. We then apply the method to real-world data, investigate the robustness and scalability of our method, consider further approaches to reduce underdetermination in the output, and perform an extensive comparison between different solvers on this inference problem. Overall, these advances build towards a full understanding of non-parametric estimation of system timescale causal structures from sub-sampled time series data.

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“…This section develops a MaxHS-like [13,33] algorithm for HornMaxSAT. In addition, the section shows that this MaxHS-like algorithm elicits the possibility of solving large scale problems with abstraction.…”

Section: Hornmaxsat Algorithm With Hitting Setsmentioning

confidence: 99%

“…It should be noted that some implementations of MaxHS use an ILP (Integer Linear Programming) package (e.g. CPLEX or SCIP) [13,33] 5 , whereas others exploit SAT solvers for computing minimum hitting sets [19,20].…”

Section: A Maxhs-like Hornmaxsat Algorithmmentioning

confidence: 99%

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Horn Maximum Satisfiability: Reductions, Algorithms and Applications

Marques-Silva

Ignatiev

Morgado

2017

Progress in Artificial Intelligence

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Recent years have witness remarkable performance improvements in maximum satisfiability (MaxSAT) solvers. In practice, MaxSAT algorithms often target the most generic MaxSAT formulation, whereas dedicated solvers, which address specific subclasses of MaxSAT, have not been investigated. This paper shows that a wide range of optimization and decision problems are either naturally formulated as MaxSAT over Horn formulas, or permit simple encodings using Horn MaxSAT. Furthermore, the paper also shows how linear time decision procedures for Horn formulas can be used for developing novel algorithms for the Horn MaxSAT problem.

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LMHS: A SAT-IP Hybrid MaxSAT Solver

Cited by 42 publications

References 15 publications

Approximation Strategies for Incomplete MaxSAT

Approximation Strategies for Incomplete MaxSAT

A constraint optimization approach to causal discovery from subsampled time series data

Horn Maximum Satisfiability: Reductions, Algorithms and Applications

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