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

摘要

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.