help improve coding efficiency and reduce the time spent on writing code.
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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:
在全球业绩稳健增长的基础上,中国市场以持续的门店扩张、可观的客流与会员规模,支撑起公司近四成的全球新增门店任务。