Paper by Erik D. Demaine

Reference:

Erik D. Demaine, Jayson Lynch, and Jiaying Sun, “An Efficient Reversible Algorithm for Linear Regression”, in Proceedings of the 2021 International Conference on Rebooting Computing (ICRC 2021), Los Alamitos, California, November 30–December 2, 2021, pages 103–108.

BibTeX

@InProceedings{EnergyRegression_ICRC2021,
  AUTHOR        = {Erik D. Demaine and Jayson Lynch and Jiaying Sun},
  TITLE         = {An Efficient Reversible Algorithm for Linear Regression},
  BOOKTITLE     = {Proceedings of the 2021 International Conference on Rebooting Computing (ICRC 2021)},
  ADDRESS       = {Los Alamitos, California},
  MONTH         = {November 30--December 2},
  YEAR          = 2021,
  PAGES         = {103--108},

  withstudent   = 1,
  length        = {6 pages},
  doi           = {https://dx.doi.org/10.1109/ICRC53822.2021.00024},
  dblp          = {https://dblp.org/rec/conf/icrc/DemaineLS21},
  comments      = {This paper is also available from <A HREF="https://doi.org/10.1109/ICRC53822.2021.00024">IEEE</A>.},
}

Abstract:

This paper presents an efficient reversible algorithm for linear regression, both with and without ridge regression. Our reversible algorithm matches the asymptotic time and space complexity of standard irreversible algorithms for this problem. Needed for this result is the expansion of the analysis of efficient reversible matrix multiplication to rectangular matrices and matrix inversion.

Comments:

This paper is also available from IEEE.

Length:

The paper is 6 pages.

Availability:

The paper is available in PDF (219k).

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