Networked Control Systems: Laplacian Matrix and Graph Association

Related lectures (30)

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Distinguishes between subgraphs and induced subgraphs in graph theory, illustrating the construction of minimal spanning trees.

Covers topics like DFS output, edge classification, acyclic graphs, correctness, time analysis, SCCs, and the Topological Sort algorithm.

Covers graphical models for probability distributions and factor graphs representation.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Explores consensus in networked control systems through graph weight design and matrix properties.

Covers sparsest cut, NP-completeness, Bougains Theorem, and concurrent flow in graphs.

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

Explores the Laplacian matrix in electric and mechanical networks, consensus, and properties of Laplacian matrices in networked control systems.