Tight Hardness Results for Consensus Problems on Circular Strings and Time Series

Abstract

Consensus problems for strings and sequences appear in numerous application contexts, ranging among bioinformatics, data mining, and machine learning. Closing some gaps in the literature, we show that several fundamental problems in this context are NP-and W[1]-hard, and that the known (including some brute-force) algorithms are close to optimality assuming the Exponential Time Hypothesis. Among our main contributions is to settle the complexity status of computing a mean in dynamic time warping spaces which, as pointed out by Brill et al. [DMKD 2019], suffered from many unproven or false assumptions in the literature. We prove this problem to be NP-hard and additionally show that a recent dynamic programming algorithm is essentially optimal. In this context, we study a broad family of circular string alignment problems. This family also serves as a key for our hardness reductions, and it is of independent (practical) interest in molecular biology. In particular, we show tight hardness and running time lower bounds for Circular Consensus String; notably, the corresponding non-circular version is easily linear-time solvable.