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    • ▾Graph theory
      • •Matchings
      • •Minimum spanning tree
      • •Automorphisms of a graph
      • •Tournament graphs
      • ▸Basics of graph theory
        • •Intro to graphs
        • •Isomorphic graphs
        • •Walks, paths, and cycles
        • •Connected graphs
        • •Adjacency and degrees
        • •Subgraphs
        • •Graph components
        • •Adjacency lists, adjacency matrices, and incidence matrices
        • •Other simple planar graphs
        • •Regular graphs
      • •Intro to bipartite graphs
      • ▸Paths and cycles
        • ▸Eulerian cycles and paths
          • •Intro
          • •Using the theorem
        • •Hamiltonian cycles and paths
      • •Planar graphs
      • ▸Coloring
        • •Intro to vertex colorings
      • •Dijkstra's algorithm
      • •Fleury's algorithm
      • •Flows and cuts
      • •Kruskal's algorithm
      • •Minimum vertex covers
      • •Number of edges in a complete graph
      • •Perfect graph
      • •Size of tree is one less than order
      • ▸Trees
        • •Intro to trees
        • •Proving properties of trees
      • •Utilities puzzle
      • •What is a complete graph?
      • •What is a cubic graph?
      • •What is a maximal clique?
      • •What is an edge-induced subgraph?
      • •What is an irregular graph?
      • •What is a spanning subgraph?
      • •What is a vertex-induced subgraph?
      • •Intro to digraphs
      • •Combinatorics and graph theory
      • •Graceful labeling
     › Graph theory

    Minimum spanning tree

    In this lesson, you'll learn what the minimal connector problem is and a greedy algorithm for solving it. You'll also learn why the algorithm produces a tree having the smallest possible weight. This tree is not necessarily unique. In addition, you'll learn how solving the minimal connector problem can give you a lower bound for the travelling salesman problem. This can be used to prove that no Hamiltonian cycle exists less than some cost, where that cost is the solution to the minimal connector problem.