Papers › Fast, Provable Algorithms for Isotonic Regression in all ℓₚ-norms
Fast, Provable Algorithms for Isotonic Regression in all ℓₚ-norms
Rasmus Kyng, Anup Rao, Sushant Sachdeva
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 ℓₚ-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections