  We compare the performance of three nearest neighbor search algorithms: the
Orchard, ball tree, and VP-tree algorithms. These algorithms are commonly used
for nearest-neighbor searches and are known for their efficiency in large
datasets. We analyze the fraction of distances computed in relation to the size
of the dataset and its dimension. For each algorithm we derive a fitting
function for the efficiency as a function to set size and dimension. The
article aims to provide a comprehensive analysis of the performance of these
algorithms and help researchers and practitioners choose the best algorithm for
their specific application.
