On the performance of MaxSAT and MinSAT solvers on 2SAT-MaxOnes

We analyze and compare two solvers for Boolean optimization problems: WMaxSatz, a solver for Partial MaxSAT, and MinSatz, a solver for Partial MinSAT. Both MaxSAT and MinSAT are similar, but previous results indicate that when solving optimization problems using both solvers, the performance is quite different on some cases. For getting insights about the differences in the performance of the two solvers, we analyze their behaviour when solving 2SAT-MaxOnes problem instances, given that 2SAT-MaxOnes is probably the most simple, but NP-hard, optimization problem we can solve with them. The analysis is based first on the study of the bounds computed by both algorithms on some particular 2SAT-MaxOnes instances, characterized by the presence of certain particular structures. We find that the fraction of positive literals in the clauses is an important factor regarding the quality of the bounds computed by the algorithms. Then, we also study the importance of this factor on the typical case complexity of Random-p 2SAT-MaxOnes, a variant of the problem where instances are randomly generated with a probability p of having positive literals in the clauses. For the case p=0, the performance results indicate a clear advantage of MinSatz with respect to WMaxSatz, but as we consider positive values of p WMaxSatz starts to show a better performance, although at the same time the typical complexity of Random-p 2SAT-MaxOnes decreases as p increases. We also study the typical value of the bound computed by the two algorithms on these sets of instances, showing that the behaviour is consistent with our analysis of the bounds computed on the particular instances we studied first.

[1]  Felip Manyà,et al.  An Exact Inference Scheme for MinSAT , 2015, IJCAI.

[2]  Felip Manyà,et al.  New Inference Rules for Max-SAT , 2007, J. Artif. Intell. Res..

[3]  Ashish Sabharwal,et al.  Connections in Networks: Hardness of Feasibility Versus Optimality , 2007, CPAIOR.

[4]  Felip Manyà,et al.  Resolution-based lower bounds in MaxSAT , 2010, Constraints.

[5]  Josep Argelich,et al.  On Solving Boolean Multilevel Optimization Problems , 2009, IJCAI 2009.

[6]  Joao Marques-Silva,et al.  Iterative and core-guided MaxSAT solving: A survey and assessment , 2013, Constraints.

[7]  J. Håstad Clique is hard to approximate withinn1−ε , 1999 .

[8]  Inês Lynce,et al.  Haplotype Inference Combining Pedigrees and Unrelated Individuals , .

[9]  Weixiong Zhang,et al.  A Study of Complexity Transitions on the Asymmetric Traveling Salesman Problem , 1996, Artif. Intell..

[10]  Joao Marques-Silva,et al.  Core-Guided MaxSAT with Soft Cardinality Constraints , 2014, International Conference on Principles and Practice of Constraint Programming.

[11]  Josep Argelich,et al.  On 2SAT-MaxOnes with Unbalanced Polarity: from Easy Problems to Hard MaxClique Problems , 2011, CCIA.

[12]  Andreas Goerdt,et al.  A Threshold for Unsatisfiability , 1992, MFCS.

[13]  Josep Argelich,et al.  Boolean lexicographic optimization: algorithms & applications , 2011, Annals of Mathematics and Artificial Intelligence.

[14]  Zhu Zhu,et al.  Optimizing with minimum satisfiability , 2012, Artif. Intell..

[15]  Joao Marques-Silva,et al.  Spatial and temporal design debug using partial MaxSAT , 2009, GLSVLSI '09.

[16]  Josep Argelich,et al.  The First and Second Max-SAT Evaluations , 2008, J. Satisf. Boolean Model. Comput..

[17]  Nikolaj Bjørner,et al.  Maximum Satisfiability Using Cores and Correction Sets , 2015, IJCAI.

[18]  Vasco M. Manquinho,et al.  Open-WBO: A Modular MaxSAT Solver, , 2014, SAT.

[19]  Maria Luisa Bonet,et al.  SAT-based MaxSAT algorithms , 2013, Artif. Intell..

[20]  Byungki Cha,et al.  Local Search Algorithms for Partial MAXSAT , 1997, AAAI/IAAI.

[21]  Maria Luisa Bonet,et al.  Solving (Weighted) Partial MaxSAT through Satisfiability Testing , 2009, SAT.

[22]  Weixiong Zhang,et al.  Phase Transitions and Backbones of 3-SAT and Maximum 3-SAT , 2001, CP.

[23]  Enrico Giunchiglia,et al.  Solving satisfiability problems with preferences , 2010, Constraints.

[24]  Chu-Min Li,et al.  An Efficient Branch-and-Bound Algorithm Based on MaxSAT for the Maximum Clique Problem , 2010 .

[25]  Djamal Habet,et al.  On the Resiliency of Unit Propagation to Max-Resolution , 2015, IJCAI.

[26]  Johan Håstad,et al.  Clique is hard to approximate within n1-epsilon , 1996, Electron. Colloquium Comput. Complex..