Belief Propagation for Graph Coloring

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Explores the Cavity Method in Mean Field Theory, analyzing spins in an external field within a graph.

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

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

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

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

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

Covers the basics of Markov chains and their algorithmic applications.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

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

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