Subspace Consensus
This research investigates subspace consensus in matrix-weighted multi-agent networks, allowing agents to agree only on specific dimensions of their state vectors while maintaining relative configurations in others. It provides algebraic and topological conditions for achieving this dimension-specific agreement, showing how network structure and coupling weights dictate whether agents converge on a desired subspace.