  The approximate nearest neighbor (ANN) search problem is fundamental to
efficiently serving many real-world machine learning applications. A number of
techniques have been developed for ANN search that are efficient, accurate, and
scalable. However, such techniques typically have a number of parameters that
affect the speed-recall tradeoff, and exhibit poor performance when such
parameters aren't properly set. Tuning these parameters has traditionally been
a manual process, demanding in-depth knowledge of the underlying search
algorithm. This is becoming an increasingly unrealistic demand as ANN search
grows in popularity. To tackle this obstacle to ANN adoption, this work
proposes a constrained optimization-based approach to tuning quantization-based
ANN algorithms. Our technique takes just a desired search cost or recall as
input, and then generates tunings that, empirically, are very close to the
speed-recall Pareto frontier and give leading performance on standard
benchmarks.
