INTERIOR-POINT METHOD FOR LARGE-SCALE $l_1$ OPTIMIZATION

Abstract:
 In this contribution, we propose an interior-point method for
large-scale $l_1$ optimization. After a short introduction, the complete
algorithm is introduced and some implementation details are given. We prove
that this algorithm is globally convergent under standard mild assumptions.
Thus nonconvex problems can be solved successfully. The results of
computational experiments given confirm efficiency and robustness of the
proposed method.