A trust region method for minimization of nonsmooth functions with linear constraints

We introduce a trust region algorithm for minimization of nons-mooth functions with linear constraints. At each iteration, the objective function is approximated by a model fuction that satisses a set of assumptions stated recently by Qi and Sun in the context of unconstrained nonsmooth optimization. The trust region iteration begins with the resolution of an \easy problem", as in recent works of Mart nez and Santos and Friedlander, Mart nez and Santos, for smooth constrained optimization. In practical implementations we use the in-nity norm for deening the trust region, which ts well with the domain of the problem. We prove global convergence and report numerical experiments .