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Concept

Kőnig's theorem (graph theory)

Related lectures (20)

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Explores Breadth-First Search and Dijkstra's algorithm for finding shortest paths in graphs.

Explores Laplacian matrices of graphs, graph-digraph association, and physical system examples.

Covers the Marriage Problem and the Gale Shapley Algorithm to find stable matches.

Distinguishes between subgraphs and induced subgraphs in graph theory, illustrating the construction of minimal spanning trees.

Explores graphical models, factor graphs, and probabilistic inferences in complex systems.

Explores coordination among agents in networked control systems through graph theory and real-world examples.

Covers the concept of weighted graphs and the Greedy algorithm for finding a minimal spanning tree.

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

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

Covers the concept of matroids, focusing on matroid intersection and the properties of subsets of a ground set.

Introduces graph sketching, generalizations like multigraphs, and weighted graphs.

Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.

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

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores fundamental graph theory concepts, Erdős' results, Chromatic Lemma, and Union Bound theorem in graph theory.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.