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 number of breaks in derivative with constraints on the least squares error.

1 | ```
fsfused.ada(y, tau=1, s.max=20, T, eps=0.1)
``` |

`y` |
Numeric vector of inputs. |

`tau` |
Step length for searching the best model, i.e., in the t-th iteration, a model with tau*t knots will be fitted. |

`s.max` |
The maximum nubmer of knots in the piecewise constant(breaks in the (k+1)-st derivative), default is 20 |

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

`eps` |
Early stop criterion. The algorithm stops when mean squared error is less than eps |

`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. |

`beta.all` |
Solution path of fitted value, beta, corresponding to different degrees of freedom. |

`df` |
A vector giving an unbiased estimate of the degrees of freedom of the fit, i.e., the number of nonzero values in |

Canhong Wen, Xueqin Wang, Yanhe Shen, Aijun Zhang

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

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