Fast, Provable Algorithms for Isotonic Regression in all L_p-norms

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

Authors

Rasmus Kyng, Anup Rao, Sushant Sachdeva

Abstract

Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $\|x-y\|,$ for a specified norm. This paper gives improved algorithms for computing the Isotonic Regression for all weighted $\ell_{p}$-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.