第三百零三条 海事赔偿责任限制，适用法院地法律。
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For well-distributed points, nearest neighbor search is often near O(logn)O(\log n)O(logn) in practice. In the worst case (all points clustered tightly or along a line), it can degrade to O(n)O(n)O(n), but this is uncommon with typical spatial data.
this limitation has a purpose - it allows us to have a more efficient matching algorithm implementation, which i don’t think is immediately obvious and i don’t think even experts in the field fundamentally “get” this. before you “fix” the problem and turn my linear matching algorithm into a quadratic one, make sure you thoroughly understand why we have this limitation. the reason is that we have only one “kind” of match, and marking the end of the position is always correct. with arbitrary lookarounds, instead of marking the match ended here, you would have to mark the match for lookaround #7 ended here, and the match for lookaround #3 ended 5 characters ago, and the match for lookaround #12 ended 100 characters ago. i had a lot of temptation to support arbitrary lookarounds, and it really does seem like an easy fix at first glance, but the complexities arise much later in the match loop, and this is a good example of how some theory just does not hold up when you implement it. as a side note, we can actually loosen this restriction on R3 (above) which is still work in progress, but not R1, because R1 is the one that determines the start position of the match.