Improved Approximation and Scalability for Fair Max-Min Diversification

Abstract

Given an n-point metric space ({𝒳},d) where each point belongs to one of m = O(1) different categories or groups and a set of integers k₁, …, k_m, the fair Max-Min diversification problem is to select k_i points belonging to category i ∈ [m], such that the minimum pairwise distance between selected points is maximized. The problem was introduced by Moumoulidou et al. [ICDT 2021] and is motivated by the need to down-sample large data sets in various applications so that the derived sample achieves a balance over diversity, i.e., the minimum distance between a pair of selected points, and fairness, i.e., ensuring enough points of each category are included. We prove the following results:  
1) We first consider general metric spaces. We present a randomized polynomial time algorithm that returns a factor 2-approximation to the diversity but only satisfies the fairness constraints in expectation. Building upon this result, we present a 6-approximation that is guaranteed to satisfy the fairness constraints up to a factor 1-ε for any constant ε. We also present a linear time algorithm returning an m+1 approximation with exact fairness. The best previous result was a 3m-1 approximation. 
2) We then focus on Euclidean metrics. We first show that the problem can be solved exactly in one dimension. {For constant dimensions, categories and any constant ε > 0, we present a 1+ε approximation algorithm that runs in O(nk) + 2^{O(k)} time where k = k₁+…+k_m.} We can improve the running time to O(nk)+poly(k) at the expense of only picking (1-ε) k_i points from category i ∈ [m]. 
Finally, we present algorithms suitable to processing massive data sets including single-pass data stream algorithms and composable coresets for the distributed processing.