Haplotype Inference Using Propositional Satisfiability

Graça, Ana; Marques-Silva, João; Lynce, Inês

doi:10.1007/978-1-4419-6800-5_7

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…But the combination of the computational challenges such problems present, and the enormous range of significant, practical applications that can be addressed this way, makes general solvers for SAT and its friends a compelling target for research. Marques-Silva (2008) reviews applications of SAT solvers circa 2008, and the interested reader might consult work applying them to bounded model checking (Biere et al, 1999;Clarke et al, 2001), planning (Kautz and Selman, 1992;Kautz, 2006), bioinformatics (Lynce and Marques-Silva, 2006;Graça et al, 2010), allocation of radio spectrum (Fréchette et al, 2016), and software verification (Babić and Hu, 2007). A further notable application has been the solution of the Boolean Pythagorean triples problem by Heule et al (2016), resulting in what is currently considered the longest mathematical proof in history.…”

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confidence: 99%

Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT

Holden

2021

FNT in Machine Learning

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The decision problem for Boolean satisfiability, generally referred to as SAT, is the archetypal NP-complete problem, and encodings of many problems of practical interest exist allowing them to be treated as SAT problems. Its generalization to quantified SAT (QSAT) is PSPACE-complete, and is useful for the same reason. Despite the computational complexity of SAT and QSAT, methods have been developed allowing large instances to be solved within reasonable resource constraints. These techniques have largely exploited algorithmic developments; however machine learning also exerts a significant influence in the development of state-of-the-art solvers. Here, the application of machine learning is delicate, as in many cases, even if a relevant learning problem can be solved, it may be that incorporating the result into a SAT or QSAT solver is counterproductive, because the run-time of such solvers can be sensitive to small implementation changes. The application of better machine learning methods in this area is thus an ongoing challenge, with characteristics unique to the field. This work provides a comprehensive review of the research to date on incorporating machine learning into SAT and QSAT solvers, as a resource for those interested in further advancing the field.

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