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Control the Algorithm to Compute (Weighted) Least-Squares Elastic Net Estimates

Source: R/control_options.R
en_algorithm_options.Rd

The package supports different algorithms to compute the EN estimate for weighted LS loss functions. Each algorithm has certain characteristics that make it useful for some problems. To select a specific algorithm and adjust the options, use any of the en_***_options functions.

Details

  • en_lars_options(): Use the tuning-free LARS algorithm. This computes exact (up to numerical errors) solutions to the EN-LS problem. It is not iterative and therefore can not benefit from approximate solutions, but in turn guarantees that a solution will be found.

  • en_cd_options(): Use an iterative coordinate descent algorithm which needs \(O(n p)\) operations per iteration and converges sub-linearly.

  • en_admm_options(): Use an iterative ADMM-type algorithm which needs \(O(n p)\) operations per iteration and converges sub-linearly.

  • en_dal_options(): Use the iterative Dual Augmented Lagrangian (DAL) method. DAL needs \(O(n^3 p^2)\) operations per iteration, but converges exponentially.

See also

Other LS-EN algorithm options: en_admm_options(), en_cd_options(), en_dal_options(), en_lars_options()