prepare.args: Generic function for preparing the sgl call arguments
In sglOptim: Generic Sparse Group Lasso Solver

Description Usage Arguments Value Author(s) See Also

Compute and prepare the sgl call arguments for the objective function

\mathrm{loss}(\mathrm{data})(β) + λ ≤ft( (1-α) ∑_{J=1}^m γ_J \|β^{(J)}\|_2 + α ∑_{i=1}^{n} ξ_i |β_i| \right)

where \mathrm{loss} is a loss/objective function. The n parameters are organized in the parameter matrix β with dimension q\times p. The vector β^{(J)} denotes the J parameter group, the dimension of β^{(J)} is denote by d_J. The dimensions d_J must be multiple of q, and β = (β^{(1)} \cdots β^{(m)}). The group weights γ \in [0,∞)^m and the parameter weights ξ \in [0,∞)^{qp}.

1	prepare.args(data, ...)

`data`	a data object
`...`	additional parameters

`block_dim`	a vector of length m, containing the dimensions d_J of the groups (i.e. the number of parameters in the groups)
`groupWeights`	a vector of length m, containing the group weights
`parameterWeights`	a matrix of dimension q \times p, containing the parameter weights
`alpha`	the α value
`data`	the data parsed to the loss module
`group_order`	original order of the columns of β. Before sgl routines return β will be reorganized according to this order.