I tried to solve it somewhat elegantly:
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🛠️ 第三步：初始化与数据迁移。91视频对此有专业解读
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: