A variable metric method for nonsmooth convex constrained optimization

We propose an algorithm for solving nonsmooth convex constrained problems, which is close in spirit and structure to the well-developed unconstrained variable metric method. In the algorithm, time-consuming quadratic programming subproblems do not need to be solved. Furthermore, neither penalty function nor filter is employed. Global convergence of the method is established. Some encouraging preliminary computational results are reported.