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Summary: Abstract
Interest in propositional satisfiability (SAT) has been on
the rise lately, spurred in part by the recent availability of
powerful solvers that are sufficiently efficient and robust to
deal with the large-scale SAT problems that typically arise in
electronic design automation application. A frequent ques-
tion that CAD tool developers and users typically ask is
which of these various solvers is "best;" the quick answer is,
of course, "it depends." In this paper we attempt to gain
some insight into, rather than definitively answer, this ques-
tion.
Introduction. Most modern SAT algorithms can be classi-
fied as enhancements to the basic Davis Putnam (DP) back-
track search approach. The DP procedure performs a depth-
first search in the n-dimensional space of the problem vari-
ables and can be viewed as consisting of three main engines:
1) a decision engine that makes elective assignments to the
variables; 2) a deduction engine that determines the conse-
quences of these assignments, typically yielding additional
forced assignments to, i.e. implications of, other variables;
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