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Research map / Learning theory and algorithms / Combinatorial and search optimization

Improved Algorithms for Maximum Satisfiability and Its Special Cases

AAAI 2023 · Learning theory and algorithms › Combinatorial and search optimization · approach: SAT and model counting solvers · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the improved branching and reduction rules for (n, 3)-MAXSAT(n, 3)-MAXSAT · (n, 3)-MAXSATrunning time: O∗(1.191n) -> O∗(1.1749n)Helped
Use the improved branching and reduction rules for (n, 4)-MAXSAT(n, 4)-MAXSAT · (n, 4)-MAXSATrunning time: O∗(1.4254n) -> O∗(1.3803n)Helped
Use the improved bounded-occurrence algorithms to solve general MAXSATMAXSAT · MAXSATrunning time: O∗(1.0927L) -> O∗(1.0911L)Helped
Use the algorithm in terms of k for (n, 3)-MAXSAT(n, 3)-MAXSAT · (n, 3)-MAXSATrunning time: O∗(1.1554k)Helped
Use the algorithm in terms of k for (n, 4)-MAXSAT(n, 4)-MAXSAT · (n, 4)-MAXSATrunning time: O∗(1.2989k)Helped
Use the discounted-length measure instead of measuring complexity only by formula lengthThe MAXSAT algorithm · MAXSATrunning time: O∗(1.08391d), O∗(1.08391L), O∗(1.0911d), O∗(1.0911L)Helped

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