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Aandrew Baggio Sahaya Arokiadoss

Publications and source records attributed to Aandrew Baggio Sahaya Arokiadoss.

2 recordsLinked to original sources

Reduced Arc-Weight Heterogeneity for Master Stability Analysis of Directed Networks

Master stability analysis can be applied to directed networks even when their Laplacian matrices are not diagonalizable. Nevertheless, a diagonalizable Laplacian is easier to analyse because the perturbations governing synchronization separate into independent modes. This paper asks how many different positive coupling strengths are needed to obtain this simplification without adding or removing any directed connections. We first give an elementary construction showing that suitable positive weights exist for every weakly connected directed network and explain its relationship to stronger genericity results in the literature. We then introduce a measure of the smallest number of distinct coupling strengths required for diagonalizability. If a common coupling strength already produces a diagonalizable Laplacian, only one value is needed. For directed acyclic networks, we give a constructive upper bound based on the length of the longest directed path from a source. This bound is exact for rooted outward directed trees. Consequently, an outward star requires only one coupling value, whereas a directed path requires a different value on every arc. These results reduce coupling heterogeneity while retaining the simpler modal form of master stability analysis. Diagonalizability is treated as an analytical convenience, not as a sufficient condition for synchronization.

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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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