Spectral bundle methods for non-convex maximum eigenvalue functions: first-order methods

Many challenging problems in automatic control may be cast as optimization programs subject to matrix inequality constraints. Here we investigate an approach which converts such problems into non-convex eigenvalue optimization programs and makes them amenable to non-smooth analysis techniques like bundle or cutting plane methods. We prove global convergence of a first-order bundle method for programs with non-convex maximum eigenvalue functions.