A proximal linearized algorithm with a quasi distance as regularization term for minimizing a DC function (difference of two convex functions) is proposed. If the sequence generated by our algorithm is bounded, it is proved that every cluster point is a critical point of the function under consideration, even if minimizations are performed inexactly at … Read more
In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a dissimilarity measure and a statistical value. This problem, which extends the standard Multidimensional Scaling Analysis, is written as a global optimization problem whose objective is the difference of two convex functions (DC). Suitable DC … Read more
We introduce an algorithm to minimize a function of several variables with no convexity nor smoothness assumptions. The main peculiarity of our approach is the use of an the objective function model which is the difference of two piecewise affine convex functions. Bundling and trust region concepts are embedded into the algorithm. Convergence of the … Read more