IntervalRegressionInternal: IntervalRegressionInternal
In tdhock/penaltyLearning: Penalty Learning

IntervalRegressionInternal

R Documentation

IntervalRegressionInternal

Description

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.

Usage

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)

Arguments

`features`	Scaled numeric feature matrix (problems x `features`). The first column/feature should be all ones and will not be regularized.
`targets`	Numeric target matrix (problems x 2).
`initial.param.vec`	initial guess for weight vector (`features`).
`regularization`	Degree of L1-regularization.
`threshold`	When the stopping criterion gets below this `threshold`, the algorithm stops and declares the solution as optimal.
`max.iterations`	If the algorithm has not found an optimal solution after this many iterations, increase `Lipschitz` constant and `max.iterations`.
`weight.vec`	A numeric vector of weights for each training example.
`Lipschitz`	A numeric scalar or NULL, which means to compute `Lipschitz` as the mean of the squared L2-norms of the rows of the feature matrix.
`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 `Lipschitz` (smaller step size) if crit gets larger than this.