@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",
}