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:
Most of the fastest-growing U.S. companies didn’t raise VC early. They didn’t need to. They fueled growth with something far more sustainable: paying customers.
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