Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms

Author name not available (Why is that?)

Publication date: 2 July 2015

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

e l l_{p}

-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.

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