Graph Coloring: Random vs Symmetrical

Related lectures (30)

Page 1 of 3

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Covers the analysis of cluster complexity in random graph coloring.

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

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

Focuses on open problems and the importance of reformulating problems with better tools and intuition.

Explores Markov chains, focusing on the coloring problem and algorithm analysis.