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Networked Control Systems: Graph Theory and Stochastic Matrices
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Related lectures (30)
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Graph Algorithms: Modeling and Representation
Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.
Laplacian Matrix: Properties and Examples
Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.
Cheeger's Inequalities
Explores Cheeger's inequalities for random walks on graphs and their implications.
Networked Control Systems: Properties of Laplacian Matrices
Explores Laplacian matrix properties in networked control systems and their relation to graph theory.
Building Ramanujan Graphs
Explores the construction of Ramanujan graphs using polynomials and addresses challenges with the probabilistic method.
Algebraic Graph Theory: Matrices and Connectivity
Explores algebraic graph theory applied to networked control systems and consensus algorithms.
Matrix Tree Theorem
Explores the Matrix Tree Theorem and its application in calculating spanning trees in graphs.
Graph Theory Fundamentals
Covers the fundamentals of graph theory, including vertices, edges, degrees, walks, connected graphs, cycles, and trees, with a focus on the number of edges in a tree.
Networked Control Systems: Laplacian Matrix and Consensus
Explores the Laplacian matrix and consensus in networked control systems.
Graph Algorithms II: Traversal and Paths
Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.