Global Practical Node and Edge Synchronization in Kuramoto Networks: A Submodular Optimization Framework
For engineers designing synchronization in complex networks (e.g., power grids, neuronal networks), this provides a principled, efficient method with theoretical guarantees for input node selection.
The paper develops a submodular optimization framework for selecting a minimal set of nodes to pin with external inputs to guarantee global synchronization in Kuramoto networks with heterogeneous node dynamics, achieving provable optimality bounds. Numerical studies on power, wireless, and neuronal networks demonstrate the approach.
Synchronization underlies phenomena including memory and perception in the brain, coordinated motion of animal flocks, and stability of the power grid. These synchronization phenomena are often modeled through networks of phase-coupled oscillating nodes. Heterogeneity in the node dynamics, however, may prevent such networks from achieving the required level of synchronization. In order to guarantee synchronization, external inputs can be used to pin a subset of nodes to a reference frequency, while the remaining nodes are steered toward synchronization via local coupling. In this paper, we present a submodular optimization framework for selecting a set of nodes to act as external inputs in order to achieve synchronization from almost any initial network state. We derive threshold-based sufficient conditions for synchronization, and then prove that these conditions are equivalent to connectivity of a class of augmented network graphs. Based on this connection, we map the sufficient conditions for synchronization to constraints on submodular functions, leading to efficient algorithms with provable optimality bounds for selecting input nodes. We illustrate our approach via numerical studies of synchronization in networks from power systems, wireless networks, and neuronal networks.