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:
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The simplest approach is to check every single point. Compute the distance from the user's location to every restaurant in the database, keep the ones that are close enough, and throw away the rest.
МИД Азербайджана отреагировал на атаки иранских дронов14:03