Markov Chains and Algorithm Applications

Related lectures (30)

Page 2 of 3

Introduces graph theory basics, Ramsey theory, and graph coloring concepts.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

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

Covers graphical models for probability distributions and factor graphs representation.

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

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Introduces unweighted bipartite matching and its solution using linear programming and the simplex method.

Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.

Explores coordination in networked control systems, graph theory, and consensus algorithms.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.