Kneeliverse: A universal knee-detection library for performance curves
Antunes, M.
; Estro,, T.
; Bhandari, P.
; Gandhi, A.
; Kuenning, G.
; Liu, Y.
; Waldspurger, C.
; Wildani, A.
; Zadok, E.
SoftwareX Vol. 30, Nº , pp. 102161 - 102161, May, 2025.
ISSN (print): 2352-7110
ISSN (online):
Scimago Journal Ranking: 0,50 (in 2025)
Digital Object Identifier: 10.1016/j.softx.2025.102161
Abstract
Identifying knee and elbow points in performance curves is a critical task in various domains, including machine learning and system design. These points represent optimal trade-offs between cost and performance, facilitating efficient decision-making and resource allocation. However, accurately determining the knees and elbows in curves poses a significant challenge. To address this challenge, we introduce Kneeliverse , an open-source library dedicated to knee/elbow point detection. Kneeliverse incorporates a suite of well-established knee-detection algorithms, including Menger, L-method, Kneedle, and DFDT. Additionally, Kneeliverse extends these algorithms to detect multiple knees and elbows in complex curves, employing a recursive approach. Kneeliverse further includes Z-Method, a recently developed algorithm specifically designed for multi-knee detection.
