Mixed-integer linearity in nonlinear optimization: a trust region approach
Bringing together nonlinear optimization with mixed-integer linear constraints enables versatile modeling, but poses significant computational challenges. We investigate a method to solve these problems based on sequential mixed-integer linearization with trust region safeguard, computing feasible iterates via calls to a generic mixed-integer linear solver. Convergence to critical, possibly suboptimal, feasible points is established for arbitrary starting points. Finally, we present numerical applications in nonsmooth optimal control and optimal network design and operation.