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:
// 作用：通过最值判断是否需要扩展左/右边界（左侧最小值/右侧<最大值的元素都需纳入无序区间）
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