Mengran Xue

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— We take a structural approach to the problem of designing the edge weights in an undirected graph subject to an upper bound on their total, so as to maximize the algebraic connectivity. Specifically, we first characterize the eigenvector(s) associated with the algebraic connectivity at the optimum, using optimization machinery together with eigenvalue(More)
Motivated by both distributed-computation and decentralized-control applications, we study distributed linear iterative algorithms with memory. Specifically, we show that the system of linear equations Gx = b can be solved through a distributed linear iteration for arbitrary invertible G, using only a single memory element at each processor. Further, we(More)
— Increasingly, teams of self-coordinating autonomous vehicles are being used in lieu of manned transport in hostile environments, and hence characterizing the security and robustness of these autonomous vehicle networks (AVNs) from uncertainty and adversarial conduct is becoming paramount. Using a canonical double-integrator network model, we study(More)
Motivated mainly by infrastructure-network management problems, our group has been pursuing analysis and design of various models for network dynamics, which vary in their specifics but broadly can be viewed as either stochastic flow or synchronization processes defined on a graph. So as to obtain a common framework for these models, here we introduce broad(More)
— In this article, we propose two stabilizing discrete-time model predictive control (MPC) strategies, which are alternatives to other classical (e.g. terminal cost/constraint-based) approaches. Both proposed strategies take advantage of a known stabilizing controller and its associated Lyapunov function. The first strategy allows optimization of an(More)