PACLasso: Penalized and Constrained Lasso Optimization
An implementation of both the equality and inequality constrained lasso
functions for the algorithm described in "Penalized and Constrained Optimization"
by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019;
see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper).
The algorithm here is designed to allow users to define linear constraints (either equality
or inequality constraints) and use a penalized regression approach to solve the constrained
problem. The functions here are used specifically for constraints with the lasso formulation,
but the method described in the PaC paper can be used for a variety of scenarios. In addition
to the simple examples included here with the corresponding functions, complete code to
entirely reproduce the results of the paper is available online through the Journal of the
American Statistical Association.
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