# What is a good way to select a small subset (say 50) of items from a large pool of items (say 5 million) while minimizing an objective function?

I have 5 million items that have 10 features (all continuous and not categorical) each and would like to select a small subset of these items. Ideally, I want to manually specify 10 features of my own up front and find a subset of items in the pool that are all close to the features I specify. By closest, I mean the distance between the numbers I specify and the item features selected from the pool.

Since your parameters are continuous, you may define a gridding of the 10-dimensional feature space with grid spacing $d_i,i=1,\ldots,10$. Find the grid coordinates of each item by dividing the feature values $x_i$ by each $d_i$ and taking the floor of the result. Bin together items with the same grid coordinates. Next, for each target feature such as you've described, you can quickly compute its grid coordinates in the same manner and select items from that cell or its neighbors to quickly find the subsets you're looking for without having to compute all 5 million (or however many) distances every time you want to specify a feature.