Networked Control Systems: Graph Theory and Stochastic Matrices

Related lectures (30)

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Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores isogenic graphs, spectral properties, and mathematical applications in modular forms and cryptography.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

Explores coordination in networked control systems, graph theory, and consensus algorithms.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Explores the application of matrices and eigendecompositions in networks.

Covers graphical models for probability distributions and factor graphs representation.