• Corpus ID: 1650609

Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering

@article{Han2015PrimalDualAM,
  title={Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering},
  author={Zheng Han and Frank E. Curtis},
  journal={ArXiv},
  year={2015},
  volume={abs/1508.02452}
}

• Zheng Han, Frank E. Curtis
• Published 10 August 2015
• Computer Science
• ArXiv

Isotonic regression (IR) is a non-parametric calibration method used in supervised learning. For performing large-scale IR, we propose a primal-dual active-set (PDAS) algorithm which, in contrast to the state-of-the-art Pool Adjacent Violators (PAV) algorithm, can be parallized and is easily warm-started thus well-suited in the online settings. We prove that, like the PAV algorithm, our PDAS algorithm for IR is convergent and has a work complexity of O(n), though our numerical experiments suggest that our PDAS algorithm is often faster than PAV. In addition, we propose PDAS variants (with safeguarding to ensure convergence) for solving related trend filtering (TF) problems, providing the results of experiments to illustrate their effectiveness.