Graph Coloring: Theory and Applications

Related lectures (29)

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Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

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

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

Covers the basics of Markov chains and their algorithmic applications.

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

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

Covers the Leighton-Rao algorithm for finding the sparsest cut in a graph, focusing on its steps and theoretical foundations.

Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.

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

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.