On Maximizing Weighted Algebraic Connectivity for Synthesizing Robust Networks

TL;DR

This paper develops efficient algorithms to optimize the algebraic connectivity of networks, enhancing robustness in UAV communication and mechanical structures, by solving complex NP-hard problems with novel iterative and heuristic methods.

Contribution

It introduces new algorithms for solving MISDPs related to maximizing algebraic connectivity, including primal-dual, cutting-plane, and heuristic approaches with theoretical guarantees.

Findings

Algorithms effectively improve network robustness.

Methods handle NP-hard optimization problems.

Enhanced convergence and solution quality.

Abstract

This article deals with the following simpler version of an open problem in system realization theory which has several important engineering applications: Given a collection of masses and a set of linear springs with a specified cost and stiffness, a resource constraint in terms of a budget on the total cost, the problem is to determine an optimal connection of masses and springs so that the resulting structure is as stiff as possible, i.e., the structure is connected and its smallest non-zero natural frequency is as large as possible. In this article, algebraic connectivity, or its mechanical analog - the smallest non-zero natural frequency of a connected structure was chosen as a performance objective. Algebraic connectivity determines the convergence rate of consensus protocols and error attenuation in Unmanned Aerial Vehicle (UAV) formations and is chosen to be a performance objective as it can be viewed as a measure of robustness in UAV communication networks to random node failures. Underlying the mechanical and UAV network synthesis problems is a Mixed Integer Semi-Definite Program (MISDP), an NP-hard problem. The novel contributions of this article lies in developing efficient algorithms to solve the MISDPs based on iterative primal-dual algorithm, cutting-plane methods for polyhedral outer-approximation, capturing the feasible set of MISDPs using Fiedler vectors or Laplacian with equivalency guarantees, and efficient neighborhood-search-based heuristic algorithms.