Limited memory bundle method for large bound constrained nonsmooth optimization: convergence analysis

Practical optimization problems often involve nonsmooth functions of hundreds or thousands of variables. As a rule, the variables in such large problems are restricted to certain meaningful intervals. In the article [N. Karmitsa and M.M. Makela, Adaptive limited memory bundle method for bound constrained large-scale nonsmooth optimization, Optimization (to appear)], we described an efficient limited-memory bundle method for large-scale nonsmooth, possibly nonconvex, bound constrained optimization. Although this method works very well in numerical experiments, it suffers from one theoretical drawback, namely, that it is not necessarily globally convergent. In this article, a new variant of the method is proposed, and its global convergence for locally Lipschitz continuous functions is proved.