A redistributed cutting plane bundle-type algorithm for multiobjective nonsmooth optimization

I construct a new cutting-plane model for approximating nonsmooth nonconvex functions in multiobjective optimization and propose a new bundle-type method with the help of an improvement function. The presented bundle method possesses three features. Firstly, the objective and constraint functions are approximated by a new cutting-plane model, which is a local convexification of the corresponding functions, instead of the entire approximation for the functions, as most bundle methods do. Secondly, the subgradients and values of the objective and constraint functions are computed approximately. In other words, approximate calculation is applied to the method, and the proposed algorithm is doubly approximate to some extent. Thirdly, the introduction of the improvement function eliminates the necessity of employing any scalarization, which is the usual method when dealing with multiobjective optimization. Under reasonable conditions satisfactory convergence results are obtained.