Consensus Algorithms: Weight Assignment and Applications

Related lectures (30)

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

Explores Cheeger's inequalities for random walks on graphs and their implications.

Covers graphical models for probability distributions and factor graphs representation.

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

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

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

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

Explores challenges and opportunities in networked control systems, covering LTI systems, delays, packet drops, and consensus.

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

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Page 2 of 2