Spherical approximations for minimax optimization problems

A general iterative method for the solution of convex minimax optimization problems is proposed. At each iteration, the functions are locally approximated by spheres and the resulting minimax problem is solved. We tested the algorithm on three different minimax location problems in the plane. Computational results with these problems are favorable.