Optimal Robust Network Design: Formulations and Algorithms for Maximizing Algebraic Connectivity

This article focuses on designing edge-weighted networks, whose robustness is characterized by maximizing algebraic connectivity, or the second smallest eigenvalue of the Laplacian matrix. This problem is motivated by cooperative vehicle localization, where accurately estimating relative position measurements and establishing communication links are essential. We also examine an associated problem where every robot is limited by payload, budget, and communication to pick no more than a specified number of relative position measurements. The basic underlying formulation for these problems is nonlinear and is known to be NP-hard. Our approach formulates this problem as a mixed-integer semidefinite program, later reformulated into a mixed-integer linear program for obtaining optimal solutions using cutting plane algorithms. We introduce a novel upper bounding algorithm based on the principal minor characterization of positive semidefinite matrices and discuss a degree-constrained lower bounding formulation inspired by robust network structures. In addition, we propose a maximum cost heuristic with low computational complexity to identify high-quality feasible solutions for instances involving up to 100 nodes. We show extensive computational results corroborating our proposed methods.