Li X, Zhang X, Dawn T (2023). fastcpd: Fast Change Point Detection via Sequential Gradient Descent. R package version 0.9.8, https://CRAN.R-project.org/package=fastcpd.

Zhang X, Dawn T (2023). “Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis.” In Ruiz, Francisco, Dy, Jennifer, van de Meent, Jan-Willem (eds.), Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 series Proceedings of Machine Learning Research, 1129–1143. https://proceedings.mlr.press/v206/zhang23b.html.

Corresponding BibTeX entries:

  @Manual{,
    title = {fastcpd: Fast Change Point Detection via Sequential
      Gradient Descent},
    author = {Xingchi Li and Xianyang Zhang and Trisha Dawn},
    year = {2023},
    note = {R package version 0.9.8},
    url = {https://CRAN.R-project.org/package=fastcpd},
  }
  @InProceedings{,
    title = {Sequential Gradient Descent and Quasi-Newton's Method for
      Change-Point Analysis},
    author = {Xianyang Zhang and Trisha Dawn},
    year = {2023},
    booktitle = {Proceedings of The 26th International Conference on
      Artificial Intelligence and Statistics},
    volume = {206},
    pages = {1129--1143},
    editor = {{Ruiz} and {Francisco} and {Dy} and {Jennifer} and {van
      de Meent} and {Jan-Willem}},
    series = {Proceedings of Machine Learning Research},
    month = {25--27 Apr},
    publisher = {PMLR},
    pdf = {https://proceedings.mlr.press/v206/zhang23b/zhang23b.pdf},
    url = {https://proceedings.mlr.press/v206/zhang23b.html},
    abstract = {One common approach to detecting change-points is
      minimizing a cost function over possible numbers and locations of
      change-points. The framework includes several well-established
      procedures, such as the penalized likelihood and minimum
      description length. Such an approach requires finding the cost
      value repeatedly over different segments of the data set, which
      can be time-consuming when (i) the data sequence is long and (ii)
      obtaining the cost value involves solving a non-trivial
      optimization problem. This paper introduces a new sequential
      updating method (SE) to find the cost value effectively. The core
      idea is to update the cost value using the information from
      previous steps without re-optimizing the objective function. The
      new method is applied to change-point detection in generalized
      linear models and penalized regression. Numerical studies show
      that the new approach can be orders of magnitude faster than the
      Pruned Exact Linear Time (PELT) method without sacrificing
      estimation accuracy.},
  }