Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:
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Games and physics simulations need to detect which objects are touching or overlapping. With nnn objects, checking every pair is O(n2)O(n^2)O(n2) comparisons, which gets expensive fast. A hundred objects means roughly 5,000 pair checks. A thousand means nearly 500,000.,详情可参考搜狗输入法2026
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