101 research outputs found
The impact of Entropy and Solution Density on selected SAT heuristics
In a recent article [Oh'15], Oh examined the impact of various key heuristics
(e.g., deletion strategy, restart policy, decay factor, database reduction) in
competitive SAT solvers. His key findings are that their expected success
depends on whether the input formula is satisfiable or not. To further
investigate these findings, we focused on two properties of satisfiable
formulas: the entropy of the formula, which approximates the freedom we have in
assigning the variables, and the solution density, which is the number of
solutions divided by the search space. We found that both predict better the
effect of these heuristics, and that satisfiable formulas with small entropy
`behave' similarly to unsatisfiable formulas
New Boolean satisfiability problem heuristic strategy: Minimal Positive Negative Product Strategy
This study presents a novel heuristic algorithm called the "Minimal Positive
Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean
satisfiability problem. It provides a mathematical explanation for the
superiority of this algorithm over widely used heuristics such as the Dynamic
Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum
(VSIDS). Experimental results further confirm the effectiveness of this
heuristic strategy in problem-solving.Comment: 7 pages, 2 figure
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