Networked Control Systems: Graph Theory and Stochastic Matrices

Related lectures (30)

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Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Introduces graph theory basics, graph representation methods, and traversal algorithms like BFS and DFS.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

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