Concept

Edge cover

Related lectures (29)

Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

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

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

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

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

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

Explores a variety of open problems in graph theory and computational complexity, challenging students to analyze and solve complex issues.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Introduces Vectorworks Educational Version for architectural drawings, covering tools, layers, and annotations.

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

Explores the union and join operations on graphs, illustrating their definitions and properties.

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

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

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

Covers variational inference and neural networks for classification tasks.

Covers regular and self-complementary graphs, exploring their properties and examples.

Covers the fundamental notion of composition in directed graphs and provides examples of their representation.

Covers the fundamentals of integer optimization, including integer programming, dynamic programming, and approximation algorithms.

Explores a classic handshake problem at a party using graph representation.