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Aandrew Baggio Sahaya Arokiadoss

Publications and source records attributed to Aandrew Baggio Sahaya Arokiadoss.

2 recordsLinked to original sources

Positive Arc-Weight Design Makes Every Directed Laplacian Diagonalizable

For directed networks, the Laplacian need not be diagonalizable, so the standard master-stability variational equations cannot in general be fully decoupled into independent eigenmodes. We prove that this obstruction can always be removed by coupling-strength design: every weakly connected digraph admits a strictly positive weighting of its existing arcs for which the weighted in-degree Laplacian is diagonalizable. The construction uses a spanning directed acyclic subgraph with one source in each root strongly connected component, assigns distinct positive weighted indegrees to its non-source vertices, and then restores all remaining arcs with a common sufficiently small positive weight. The zero eigenvalue remains semisimple and all nonzero eigenvalues remain simple. We also give a discriminant criterion that computes an admissible interval of restoring weights. Thus, any fixed weakly connected directed topology can be positively weighted so that master-stability perturbations admit a complete modal decomposition.

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A (Purely) Graph-Theoretic Approach to Synchronization of Nonlinear Dynamical Networks

Synchronizing nonlinear dynamical networks typically requires solving matrix inequalities or detailed system models, which fail for large networks. This paper offers a simple fix : a purely graph-theoretic framework using only a single Lipschitz-like bound on the dynamics. Coupling strengths are computed directly from the digraph, bypassing inequality solvers entirely. The method succeeds where existing approaches encounter infeasibility due to connectivity patterns. It examines only $n-1$ directed paths per strongly connected component versus $\frac{n(n-1)}{2}$ undirected paths before, achieving $O(n^3)$ complexity. Results show network connectivity can be exploited to synchronize a large class of nonlinear dynamical networks.

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