Networked Control Systems: Convergence Rate and Digraphs

Related lectures (27)

Page 2 of 3

Explores time-varying consensus algorithms in networked control systems and the Laplacian matrix's role in achieving average consensus.

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

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Covers the basics and applications of graph coloring, including balancing vectors and achieving perfect fairness.

Explores real functions, their graphs, properties, and transformations, including symmetry and surjection.

Explores Ramsey theory, alterations, colorings in graphs, monochromatic matchings, and the significance of large cliques.

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

Explores Laplacian operators on graphs and the model of DC microgrids.

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