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:
Трамп высказался о непростом решении по Ирану09:14
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「因為我當時就覺得，我來美國都已經三、四年了，而且也沒有犯罪紀錄，而且我也在正常工作、報稅，所以我覺得不會專門跑來抓我。」
[2] K. Lemström & P. Fränti: “N-Candidate methods for location invariant dithering of color images” (2000). ↑
elsewhere in my program.