In this paper, we extend the smooth self-adoptive trust region technique for minimization of locally Lipschitz functions. Despite existing nonsmooth trust region methods, we employ self-adoptive updates in current iterative and trust region radius in nonsmooth trust region method. Also, to improve the performance of the algorithm, we use the linear search algorithm. This causes a signi cant decrease in the number of subproblem that need to be solved and, consequently, a decrease in the number of function evaluations. Under some standard assumptions, the global convergence property of the new proposed method is established for minimization of locally Lipschitz functions. The proposed algorithm is implemented in MATLAB environment and applied on some test problems. Numerical results con rm the efficiency of the self-adaptive nonsmooth trust region method with line search in comparison with nonsmooth trust region method on some standard nonsmooth test functions.
