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We are given a set $S$ of $m\gg 1$ points in $\mathbb{R}^n$.

In the problem I am trying to solve, in a sequential fashion, we obtain a new point $p_r\not\in S$ at each round $r\ge 1$ and the goal is to find the point $s_r\in S$ closest to $p_r$ in $S$, possibly in an approximate way, according to the Euclidean distance.


Question: How can we preprocess and organize the information of the points in $S$, to solve this problem focusing on the trade-off between time complexity and distance minimization?

I guess we can use sampling techniques and randomized algorithms/data structures, to obtain a solution with theoretical performance guarantees in expectation (or with high probability) over the internal algorithmic randomization. Is there in the related literature any solution already found for this problem?

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    $\begingroup$ I think this type of problem usually goes under the name (vector) quantization. There are some well established preprocessing approaches, e.g., k-d trees. Since nearest-neighbor models are standard examples in machine learning, you can find lots of useful explainers online (I found some by googling "nearest neighbor quantization k-d trees"). $\endgroup$ Feb 23 at 16:12
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    $\begingroup$ $p_r=s_r$?$\,\,\,\,\,\,\,$ $\endgroup$ Feb 23 at 16:58
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    $\begingroup$ @DieterKadelka thank you for your question. No, we just have to find the closest point in $S$ at each round and the main computational issue is that $S$ is too large to check all points it contains. $\endgroup$ Feb 23 at 17:35
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    $\begingroup$ Maybe I'm confused. I don't think you can mean "find the point closest to $s_r$ in $S$". Probably you mean "find the point in $S$ which is closest to $p_r$"? $\endgroup$ Feb 23 at 19:51
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    $\begingroup$ OK @MichaelEngelhardt I will think about it. I have the feeling that if we have an (adversarial arrangements of points) similar somewhat to the one I attempted to describe here mathoverflow.net/q/383900/115803, it is still unlikely this strategy can work, but maybe I am wrong. In any case, it would be interesting and useful to find a rigorous characterization of these adversarial point arrangements in $S$, to be able to identify a wide class of inputs where this operations can be done in a computational efficient way. $\endgroup$ Feb 23 at 21:04
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This is a complex but well-studied topic. Perhaps starting here (Chapter 43) will help you focus on your particular application:

Andoni, Alexandr, and Piotr Indyk. "Nearest neighbors in high-dimensional spaces." Handbook of Discrete and Computational Geometry. (2017). PDF of preliminary version of Ch.43.

Here's a little snippet:

Almost all algorithms for proximity problems in high-dimensional spaces proceed by reducing the problem to the problem of finding an approximate near neighbor, which is the decision version of the approximate nearest-neighbor problem. [...] All the NNS algorithms are based on space partitions (even if not always framed this way). We distinguish two broad classes of partitions: 1) data-independent approaches, where the partition is independent of the given dataset $P$, and 2) data-dependent approaches, where the partition depends on the dataset $P$.

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  • $\begingroup$ Thank you for the reference. I will read it carefully. $\endgroup$ Feb 23 at 21:05

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