The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
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I learned that for 4-SAT, if clause to variable ratio is more than 10, the generated problems become difficult to solve, and the likelihood of formula to be SAT or UNSAT is close to 50%. So I generated 3 types of formulas:
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