Abstract
A Minimal Correction Subset (MCS) of an unsatisfiable constraint set is a minimal subset of constraints that, if removed, makes the constraint set satisfiable. MCSs enjoy a wide range of applications, one of them being approximate solutions to constrained optimization problems. However, existing work on applying MCS enumeration to optimization problems focuses on the single-objective case.
In this work, a first definition of Pareto Minimal Correction Subsets (Pareto-MCSs) is proposed with the goal of approximating the Pareto-optimal solution set of multi-objective constrained optimization problems. We formalize and prove an equivalence relationship between Pareto-optimal solutions and Pareto-MCSs. Moreover, Pareto-MCSs and MCSs can be connected in such a way that existing state-of-the-art MCS enumeration algorithms can be used to enumerate Pareto-MCSs.
An experimental evaluation considers the multi-objective virtual machine consolidation problem. Results show that the proposed Pareto-MCS approach outperforms the state-of-the-art approaches.
The authors would like to thank Salvador Abreu and Pedro Salgueiro from Universidade de Évora for granting the authors permission to use their cluster. This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/CEC/50021/2013 and SFRH/BD/111471/2015.
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References
Bacchus, F., Davies, J., Tsimpoukelli, M., Katsirelos, G.: Relaxation search: a simple way of managing optional clauses. In: 28th Conference on Artificial Intelligence, pp. 835–841. AAAI (2014)
Bailey, J., Stuckey, P.J.: Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization. In: Hermenegildo, M.V., Cabeza, D. (eds.) PADL 2005. LNCS, vol. 3350, pp. 174–186. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30557-6_14
Ben-Eliyahu, R., Dechter, R.: On computing minimal models. Ann. Math. Artif. Intell. 18(1), 3–27 (1996)
Birnbaum, E., Lozinskii, E.L.: Consistent subsets of inconsistent systems: structure and behaviour. J. Exp. Theoret. Artif. Intell. 15(1), 25–46 (2003)
Bjørner, N., Phan, A.-D., Fleckenstein, L.: vZ - an optimizing SMT solver. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 194–199. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46681-0_14
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83
Fan, X., Weber, W., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: 34th International Symposium on Computer Architecture, pp. 13–23 (2007)
Felfernig, A., Schubert, M., Zehentner, C.: An efficient diagnosis algorithm for inconsistent constraint sets. Artif. Intell. Eng. Des. Anal. Manuf. 26(1), 53–62 (2012)
Grégoire, É., Lagniez, J., Mazure, B.: An experimentally efficient method for (mss, comss) partitioning. In: 28th Conference on Artificial Intelligence, pp. 2666–2673. AAAI (2014)
Ignatiev, A., Janota, M., Marques-Silva, J.: Towards efficient optimization in package management systems. In: 36th International Conference on Software Engineering, pp. 745–755 (2014)
Junker, U.: QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems. In: 19th National Conference on Artificial Intelligence, 16th Conference on Innovative Applications of Artificial Intelligence, pp. 167–172 (2004)
Le Berre, D., Parrain, A.: The sat4j library, release 2.2. J. Satisfiab. Bool. Model. Comput. 7(2–3), 59–64 (2010)
Manquinho, V., Marques-Silva, J.P., Planes, J.: Algorithms for weighted boolean optimization. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 495–508. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02777-2_45
Marques-Silva, J., Heras, F., Janota, M., Previti, A., Belov, A.: On computing minimal correction subsets. In: 23rd International Joint Conference on Artificial Intelligence, IJCAI, pp. 615–622 (2013)
Mencía, C., Previti, A., Marques-Silva, J.: Literal-based MCS extraction. In: 24th International Joint Conference on Artificial Intelligence, IJCAI, pp. 1973–1979 (2015)
Nöhrer, A., Biere, A., Egyed, A.: Managing SAT inconsistencies with HUMUS. In: 6th International Workshop on Variability Modelling of Software-Intensive Systems, pp. 83–91 (2012)
O’Callaghan, B., O’Sullivan, B., Freuder, E.C.: Generating corrective explanations for interactive constraint satisfaction. In: Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 445–459. Springer, Heidelberg (2005). doi:10.1007/11564751_34
Pareto, V.: Manuale di Economia Politica, vol. 13. Societa Editrice, Milano (1906)
Rayside, D., Estler, H.C., Jackson, D.: The guided improvement algorithm for exact, general-purpose, many-objective combinatorial optimization. Technical report. MIT-CSAIL-TR-2009-033, MIT Massachusetts Institute of Technology (2009)
Salfner, F., Tröger, P., Polze, A.: Downtime analysis of virtual machine live migration. In: 4th International Conference on Dependability, IARIA, pp. 100–105 (2011)
Ulungu, E.L., Teghem, J.: Multi-objective combinatorial optimization problems: a survey. J. Multi-Crit. Dec. Anal. 3(2), 83–104 (1994)
Xu, J., Fortes, J.A.B.: Multi-objective virtual machine placement in virtualized data center environments. In: IEEE/ACM International Conference on Green Computing and Communications, GreenCom, & International Conference on Cyber, Physical and Social Computing, CPSCom, pp. 179–188 (2010)
Zheng, Q., Li, R., Li, X., Shah, N., Zhang, J., Tian, F., Chao, K., Li, J.: Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener. Comput. Syst. 54, 95–122 (2016)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
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Terra-Neves, M., Lynce, I., Manquinho, V. (2017). Introducing Pareto Minimal Correction Subsets. In: Gaspers, S., Walsh, T. (eds) Theory and Applications of Satisfiability Testing – SAT 2017. SAT 2017. Lecture Notes in Computer Science(), vol 10491. Springer, Cham. https://doi.org/10.1007/978-3-319-66263-3_13
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