Lecture

Minimum Spanning Trees: Prim's Algorithm

Related lectures (28)

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

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

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

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

Covers the implementation and analysis of disjoint sets data structure and introduces the concept of minimum spanning trees.

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

Explores Prim's and Kruskal's algorithms for finding minimum spanning trees in a graph, covering their correctness, implementation, and analysis.

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

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

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Introduces Dijkstra's algorithm and probabilistic analysis through the Hiring Problem.

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.

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

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

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

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

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

Covers the fundamentals of connectivity in graph theory, including paths, cycles, and spanning trees.