MaxHS

MaxHS is a fast solver for a variety of optimization problems. MaxHS accepts any of the four classes of MaxSat problems including the most general weighted partial class. Unlike most other MaxSat solvers MaxHS can accept floating point weights. Represent your optimization problem as a set of soft and hard clauses. The soft clauses can have weights. MaxHS takes its input in WDIMACS format as used by the MaxSat Evauations


References in zbMATH (referenced in 21 articles , 2 standard articles )

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  1. Cai, Shaowei; Lei, Zhendong: Old techniques in new ways: clause weighting, unit propagation and hybridization for maximum satisfiability (2020)
  2. Otpuschennikov, Ilya V.; Semenov, Alexander A.: Using merging variables-based local search to solve special variants of MaxSAT problem (2020)
  3. Say, Buser; Sanner, Scott: Compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models (2020)
  4. Hooker, John N.: Logic-based Benders decomposition for large-scale optimization (2019)
  5. Zha, Aolong; Koshimura, Miyuki; Fujita, Hiroshi: (N)-level modulo-based CNF encodings of pseudo-Boolean constraints for MaxSAT (2019)
  6. Ansótegui, Carlos; Gabàs, Joel: WPM3: an (in)complete algorithm for weighted partial MaxSAT (2017)
  7. Berg, Jeremias; Järvisalo, Matti: Cost-optimal constrained correlation clustering via weighted partial maximum satisfiability (2017)
  8. Ansótegui, Carlos; Gabàs, Joel; Levy, Jordi: Exploiting subproblem optimization in SAT-based maxsat algorithms (2016)
  9. Arif, M. Fareed; Mencía, Carlos; Ignatiev, Alexey; Manthey, Norbert; Peñaloza, Rafael; Marques-Silva, Joao: BEACON: an efficient SAT-based tool for debugging (\mathcalEL^+) ontologies (2016)
  10. Berend, Daniel; Twitto, Yochai: The normalized autocorrelation length of random MAX (r)-SAT converges in probability to ((1-1/2^r)/r) (2016)
  11. Cai, Shaowei; Luo, Chuan; Lin, Jinkun; Su, Kaile: New local search methods for partial MaxSAT (2016)
  12. Hurley, Barry; O’Sullivan, Barry; Allouche, David; Katsirelos, George; Schiex, Thomas; Zytnicki, Matthias; de Givry, Simon: Multi-language evaluation of exact solvers in graphical model discrete optimization (2016)
  13. Liffiton, Mark H.; Previti, Alessandro; Malik, Ammar; Marques-Silva, Joao: Fast, flexible MUS enumeration (2016)
  14. Liu, Yan-Li; Li, Chu-Min; He, Kun; Fan, Yi: Breaking cycle structure to improve lower bound for Max-SAT (2016)
  15. Saikko, Paul; Berg, Jeremias; Järvisalo, Matti: LMHS: A SAT-IP hybrid maxsat solver (2016)
  16. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015) ioport
  17. Mangal, Ravi; Zhang, Xin; Nori, Aditya V.; Naik, Mayur: Volt: a lazy grounding framework for solving very large maxsat instances (2015)
  18. Allouche, David; André, Isabelle; Barbe, Sophie; Davies, Jessica; de Givry, Simon; Katsirelos, George; O’Sullivan, Barry; Prestwich, Steve; Schiex, Thomas; Traoré, Seydou: Computational protein design as an optimization problem (2014)
  19. Davies, Jessica; Bacchus, Fahiem: Postponing optimization to speed up MAXSAT solving (2013) ioport
  20. Morgado, Antonio; Heras, Federico; Liffiton, Mark; Planes, Jordi; Marques-Silva, Joao: Iterative and core-guided maxsat solving: a survey and assessment (2013)

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Further publications can be found at: http://www.maxhs.org/docs/papers.html