Sparse Learning with Convex and Concave Penalties
Description
Fast tools for fitting sparse generalized linear models with convex
penalties (lasso) and concave penalties (smoothly clipped absolute
deviation and minimax concave penalty). Computation uses multi-stage convex
relaxation and pathwise coordinate optimization with warm starts,
active-set updates, and screening rules. Core solvers are implemented in
C++, and coefficient paths are stored as sparse matrices for memory
efficiency.