  In this paper we develop algorithms to solve generalized weighted
Fermat-Torricelli problems with positive and negative weights and multifacility
location problems involving distances generated by Minkowski gauges. We also
introduce a new model of clustering based on squared distances to convex sets.
Using the Nesterov smoothing technique and an algorithm for minimizing
differences of convex functions called the DCA introduced by Tao and An, we
develop effective algorithms for solving these problems.
