Graph

Descripción

Graphs are a useful model of connections.
Jani Issakainen
Mapa Mental por Jani Issakainen, actualizado hace más de 1 año
Jani Issakainen
Creado por Jani Issakainen hace más de 7 años
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Resumen del Recurso

Graph
  1. Spanning tree

    Nota:

    • Whole, Acyclic subgraph. Includes all the nodes in the graph.
    1. Minimum spanning tree
      1. Prim

        Nota:

        • Greedy. We want to be adding nodes that are the closest to the Tree (T) being constructed.(NOT A NODE LIKE DJIKSTRA). So we want in every turn add minDist[] EDGE, to the tree. Means we want to know dist[] and parent! (to what node is the distance) Initialise all dist[]=inf, parent[]=nil.Start with random node.dist[rNode] = 0, Go through adj. list, update dist and parent. Here's the trick. Although the heap contains nodes (key value is their distance), tree T contains edges. So T= T u ({n,v}).
        1. Kruskal

          Nota:

          • Every node is it's own island. We connect them one by one, cheapest first. In the end, only one island remains. Remember, it's a tree we are building, so edges. for every edge we check wheter it connects to islands then we do it if it does. We take em from a heap.
          1. Union-Find
            1. find

              Nota:

              • find(x) find grandparent of x return
              1. union

                Nota:

                • Union(y,z) if(y.h y.p=z else if z.h>y.h else if EQUAL y.p=z z.h ++
                1. make-set

                  Nota:

                  • make-set(x) x.parent=x x.height = 0
          2. Shortest path
            1. Single-source
              1. Dijkstra

                Nota:

                • Optimized Relax calls. (Every Edge relaxed once)

                Adjunto:

                1. Bellman-Ford

                  Nota:

                  • Not optimized Relax calls. Max V-1 times Relax calls per edge. V-1 times      for every Edge            relax(edge) 
                2. All
                  1. Floyd-Warshall
                3. Unweighted graphs
                  1. Path finding. path[]
                    1. Breadth-first-search

                      Nota:

                      • Input: Graph and source Goal: all shortest paths known. Tools:  -queue, to store nodes in order -path array, path[u] tells the node leading to u. -distance array -visited boolean array,  or "added to que" array
                      1. Depth-first-search

                        Nota:

                        • Cycle detection. There is a cycle in a graph only if there is a backward edge in the algorithm.
                    2. Topological sort

                      Nota:

                      • for every directed edge   uv in the sorting, u comes before v. needs to be a directed acyclic graph
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