Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
@article{Kyng2015FastPA, title={Fast, Provable Algorithms for Isotonic Regression in all L_p-norms}, author={Rasmus Kyng and A. Rao and Sushant Sachdeva}, journal={ArXiv}, year={2015}, volume={abs/1507.00710} }
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.
Supplemental Code
Github Repo
Interior point method for computing the Isotonic regression on arbitrary directed acyclic graphs. Code accompanying the paper 'Fast, Provable Algorithms for Isotonic Regression in all l_p-norms' at NIPS 2015
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