Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Networked Control Systems: Laplacian Matrix and Consensus in Continuous Time
Graph Chatbot
Related lectures (29)
Previous
Page 2 of 3
Next
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.
Depth-First Search: Traversing and Sorting Graphs
Explores depth-first search, breadth-first search, graph representation, and topological sorting in graphs.
Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Probability & Stochastic Processes
Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Expander Graphs: Properties and Eigenvalues
Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.
Graph Theory and Network Flows
Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.
Consensus Algorithms: Weight Assignment and Applications
Explores the design of graph weights for consensus and applications in sensor networks.
Networked Control Systems: Coordination Among Agents
Explores coordination among agents in networked control systems through graph theory and real-world examples.
Networked Control Systems: Laplacian Operators and Microgrids
Explores Laplacian operators on graphs and the model of DC microgrids.
Graph Algorithms: Modeling and Representation
Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.