Networked Control Systems: Laplacian Matrix and Consensus

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Explores the design of graph weights for consensus and applications in sensor networks.

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

Explores the Laplacian matrix properties and consensus in continuous time.

Explores algebraic graph theory applied to networked control systems and consensus algorithms.

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

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

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

Explores depth-first search, breadth-first search, graph representation, and topological sorting in graphs.

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

Covers graphical models for probability distributions and factor graphs representation.