Prim's Algorithm This algorithm starts with one node. It then, one by one, adds a node that is unconnected to the new graph to the new graph, each time.

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Presentation transcript:

Prim's Algorithm This algorithm starts with one node. It then, one by one, adds a node that is unconnected to the new graph to the new graph, each time selecting the node whose connecting edge has the smallest weight out of the available nodes’ connecting edges.

The steps are: 1. The new graph is constructed - with one node from the old graph. 2. While new graph has fewer than n nodes, 1. Find the node from the old graph with the smallest connecting edge to the new graph, 2. Add it to the new graph Every step will have joined one node, so that at the end we will have one graph with all the nodes and it will be a minimum spanning tree of the original graph.

A BC D E F G H I J Complete Graph

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Old GraphNew Graph A BC D E F G H I J

A BC D E F G H I J Complete GraphMinimum Spanning Tree A BC D E F G H I J

Analysis of Prim's Algorithm Running Time = O(m + n log n) (m = edges, n = nodes) If a heap is not used, the run time will be O(n^2) instead of O(m + n log n). However, using a heap complicates the code since you’re complicating the data structure. A Fibonacci heap is the best kind of heap to use, but again, it complicates the code. Unlike Kruskal’s, it doesn’t need to see all of the graph at once. It can deal with it one piece at a time. It also doesn’t need to worry if adding an edge will create a cycle since this algorithm deals primarily with the nodes, and not the edges. For this algorithm the number of nodes needs to be kept to a minimum in addition to the number of edges. For small graphs, the edges matter more, while for large graphs the number of nodes matters more.