Recursive l(sub 1, infinity) Group lasso
MICHIGAN UNIV ANN ARBOR DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal Lsub 1, infinity-penalized recursive least squares RLS predictor. Each update minimizes a convex but nondifferentiable function optimization problem. We develop an online homotopy method to reduce the computational complexity. Numerical simulations demonstrate that the proposed algorithm outperforms the lsub 1 regularized RLS algorithm for a group sparse system identification problem and has lower implementation complexity than direct group lasso solvers.
- Numerical Mathematics