Trust-region interior-point method for large sparse l1 optimization

In this article, we propose an interior- point method for large sparse 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, relatively difficult l(1) optimization problems can be solved successfully. The results of computational experiments given in this article confirm efficiency and robustness of the proposed method.