@techreport{iiasa4196,
           month = {March},
            type = {IIASA Working Paper},
           title = {A Bundle of Method for Minimizing a Sum of Convex Functions with Smooth Weights},
         address = {IIASA, Laxenburg, Austria},
       publisher = {WP-94-013},
            year = {1994},
             url = {https://pure.iiasa.ac.at/id/eprint/4196/},
        abstract = {We give a bundle method for minimizing a (possibly nondifferentiable and nonconvex) function h(z) = sum\_\{i=1\}{\^{ }}m p\_i(x) f\_i(x) over a closed convex set in R{\^{ }}n, where p\_i are nonnegative and smooth and f\_i are finite-valued convex. Such functions arise in certain stochastic programming problems and scenario analysis. The method finds search directions via quadratic programming, using a polyhedral model of h that involves current linearizations of p\_i and polyhedral models of f\_i based on their accumulated subgradients. We show that the method is globally convergent to stationary points of h. The method exploits the structure of h and hence seems more promising than general-purpose bundle methods for nonconvex minimization.},
          author = {Kiwiel, K.}
}