Description Usage Arguments Value Author(s) References See Also Examples

This is a function that solves the L0 fused problem via the primal dual active set algorithm in sparse condition. It fits a piecewise constant regression model by minimizing the least squares error with constraints on the number of breaks in their discrete derivative.

1 | ```
fsfused(y, s = 10, T, K.max=5)
``` |

`y` |
Response sequence to be fitted. |

`s` |
Number of knots in the piecewise constant(breaks in the derivative), default is 10. |

`T` |
Number of non-zero values in fitted coefficient. |

`K.max` |
The maximum number of steps for the algorithm to take before termination. Default is 5. |

`y` |
The observed response vector. Useful for plotting and other methods. |

`beta` |
Fitted value. |

`v` |
Primal coefficient. The indexes of the nonzero values correspond to the locations of the breaks. |

Canhong Wen, Xueqin Wang, Yanhe Shen, Aijun Zhang

Wen,C., Wang, X., Shen, Y., and Zhang, A. (2017). "L0 trend filtering", technical report.

1 2 3 4 5 6 7 8 9 |

FastSF documentation built on July 19, 2017, 9:02 a.m.

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