grpl.control {grplasso}R Documentation

Options for the Group Lasso Algorithm

Description

Definition of options such as bounds on the Hessian, convergence criteria and output management for the Group Lasso algorithm.

Usage

grpl.control(save.x = FALSE, save.y = TRUE,
             update.hess = c("lambda", "always"), update.every = 3,
             inner.loops = 10, line.search = TRUE, max.iter = 500,
             tol = 5 * 10^-8, lower = 10^-2, upper = Inf, beta = 0.5,
             sigma = 0.1, trace = 1)

Arguments

save.x a logical indicating whether the design matrix should be saved.
save.y a logical indicating whether the response should be saved.
update.hess should the hessian be updated in each iteration ("always")? update.hess = "lambda" will update the Hessian once for each component of the penalty parameter "lambda" based on the parameter estimates corresponding to the previous value of the penalty parameter.
update.every Only used if update.hess = "lambda". E.g. set to 3 if you want to update the Hessian only every third grid point.
inner.loops How many loops should be done (at maximum) when solving only the active set (without considering the remaining predictors). Useful if the number of predictors is large. Set to 0 if no inner loops should be performed.
line.search Should line searches be performed?
max.iter Maximal number of loops through all groups
tol convergence tolerance; the smaller the more precise, see details below.
lower lower bound for the diagonal approximation of the corresponding block submatrix of the Hessian of the negative log-likelihood function.
upper upper bound for the diagonal approximation of the corresponding block submatrix of the Hessian of the negative log-likelihood function.
beta scaling factor β < 1 of the Armijo line search.
sigma 0 < σ < 1 used in the Armijo line search.
trace integer. 0 omits any output, 1 prints the current lambda value, 2 prints the improvement in the objective function after each sweep through all the parameter groups and additional information.

Details

For the convergence criteria see chapter 8.2.3.2 of Gill et al. (1981).

Value

An object of class grpl.control.

References

Philip E. Gill, Walter Murray and Margaret H. Wright (1981) Practical Optimization, Academic Press.

Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.


[Package grplasso version 0.4-2 Index]