Description Usage Arguments Value Author(s)

Solve the squared hinge loss interval regression problem for one
`regularization`

parameter: w* = argmin_w L(w) + `regularization`

*
||w||_1 where L(w) is the average squared hinge loss with respect
to the `targets`

, and ||w||_1 is the L1-norm of the weight vector
(excluding the first element, which is the un-regularized
intercept or bias term). This function performs no scaling of
input `features`

, and is meant for internal use only! To learn a
regression model, try `IntervalRegressionCV`

or
`IntervalRegressionUnregularized`

.

1 2 3 4 5 6 7 8 | ```
IntervalRegressionInternal(features,
targets, initial.param.vec,
regularization, threshold = 0.001,
max.iterations = 1000,
weight.vec = NULL,
Lipschitz = NULL,
verbose = 2, margin = 1,
biggest.crit = 100)
``` |

`features` |
Scaled numeric feature matrix (problems x |

`targets` |
Numeric target matrix (problems x 2). |

`initial.param.vec` |
initial guess for weight vector ( |

`regularization` |
Degree of L1-regularization. |

`threshold` |
When the stopping criterion gets below this |

`max.iterations` |
If the algorithm has not found an optimal solution after this many
iterations, increase |

`weight.vec` |
A numeric vector of weights for each training example. |

`Lipschitz` |
A numeric scalar or NULL, which means to compute |

`verbose` |
Cat messages: for restarts and at the end if >= 1, and for every iteration if >= 2. |

`margin` |
Margin size hyper-parameter, default 1. |

`biggest.crit` |
Restart FISTA with a bigger |

Numeric vector of scaled weights w of the affine function f_w(X) = X %*% w for a scaled feature matrix X with the first row entirely ones.

Toby Dylan Hocking

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