@ARTICLE{antunes2025softwarex,
  AUTHOR =	 {M{\'a}rio Antunes and Tyler Estro and Pranav Bhandari and
                  Anshul Gandhi and Geoff Kuenning and Yifei Liu and Carl
                  Waldspurger and Avani Wildani and Erez Zadok},
  JOURNAL =	 {SoftwareX},
  TITLE =	 "Kneeliverse: A Universal Knee-Detection Library for
                  Performance Curves",
  YEAR =	 2025,
  ISSN =	 {2352-7110},
  PAGES =	 102161,
  VOLUME =	 30,
  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.},
  DOI =		 {https://doi.org/10.1016/j.softx.2025.102161},
  KEYWORDS =	 {Knee estimation, Multi-knee estimation, Optimization,
                  Python},
  URL =
                  {https://www.sciencedirect.com/science/article/pii/S2352711025001281},
}