Concept

Bridge (graph theory)

Related lectures (18)

Discusses Low Diameter Randomized Decomposition and graph partitioning for edge cuts and coloring.

Explores stereo vision concepts such as occlusions, window size impact, multi-view stereo, dynamic shape reconstruction, and graph-based segmentation.

Segmentation: Theory and Algorithms

Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.

Topological Sort and SCC

Explores topological sort, acyclic graphs, Strongly Connected Components, magic algorithm, component graph, flow networks, and their applications.

Cheeger's Inequality

Explores Cheeger's inequality and its implications in graph theory.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Graph Algorithms: Basics

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

Algebraic Graph Theory: Matrices and Connectivity

Explores algebraic graph theory applied to networked control systems and consensus algorithms.

Networked Control Systems: Properties of Laplacian Matrices

Explores Laplacian matrix properties in networked control systems and their relation to graph theory.

Max-flow and Disjoint Sets

Explores the Ford-Fulkerson method, max-flow, applications of max-flow, and the disjoint-set data structure.

Coxeter Groups: Spectral Theorem and Sylvester's Criterion

Explores the spectral theorem, Coxeter graphs, eigenvalues, and determinants of positive definite matrices.

Laplacian Matrix: Properties and Examples

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

Algorithms: Union Find and Minimum Spanning Trees

Discusses Union-Find data structures and Minimum Spanning Trees, covering algorithms and their applications in network design and optimization.

Graph Algorithms: Ford-Fulkerson and Strongly Connected Components

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

Max-flow Min-cut Theorem

Explores the equivalence between maximum flow and minimum cut in network theory, demonstrating its applications through examples and edge-disjoint paths.

Graph Algorithms: Flows and Strongly Connected Components

Discusses graph algorithms, focusing on flow networks and strongly connected components.

Max-flow Min-cut Theorem

Explores the Max-flow Min-cut theorem, integral capacities, Ford-Fulkerson method, bipartite matching, and edge-disjoint paths.