Difference between Prim's and Dijkstra's algorithms?

Question

What is the exact difference between Dijkstra\'s and Prim\'s algorithms? I know Prim\'s will give a MST but the tree generated by Dijkstra will also be a MST. Then what is

Answer 1

The only difference I see is that Prim's algorithm stores a minimum cost edge whereas Dijkstra's algorithm stores the total cost from a source vertex to the current vertex.

Dijkstra gives you a way from the source node to the destination node such that the cost is minimum. However Prim's algorithm gives you a minimum spanning tree such that all nodes are connected and the total cost is minimum.

In simple words:

So, if you want to deploy a train to connecte several cities, you would use Prim's algo. But if you want to go from one city to other saving as much time as possible, you'd use Dijkstra's algo.

Answer 2

The key difference between the basic algorithms lies in their different edge-selection criteria. Generally, they both use a priority queue for selecting next nodes, but have different criteria to select the adjacent nodes of current processing nodes: Prim's Algorithm requires the next adjacent nodes must be also kept in the queue, while Dijkstra's Algorithm does not:

def dijkstra(g, s):
    q <- make_priority_queue(VERTEX.distance)
    for each vertex v in g.vertex:
        v.distance <- infinite
        v.predecessor ~> nil
        q.add(v)
    s.distance <- 0
    while not q.is_empty:
        u <- q.extract_min()
        for each adjacent vertex v of u:
            ...

def prim(g, s):
    q <- make_priority_queue(VERTEX.distance)
    for each vertex v in g.vertex:
        v.distance <- infinite
        v.predecessor ~> nil
        q.add(v)
    s.distance <- 0
    while not q.is_empty:
        u <- q.extract_min()
        for each adjacent vertex v of u:
            if v in q and weight(u, v) < v.distance:// <-------selection--------
            ...

The calculations of vertex.distance are the second different point.

Answer 3

Both can be implemented using exactly same generic algorithm as follows:

Inputs:
  G: Graph
  s: Starting vertex (any for Prim, source for Dijkstra)
  f: a function that takes vertices u and v, returns a number

Generic(G, s, f)
    Q = Enqueue all V with key = infinity, parent = null
    s.key = 0
    While Q is not empty
        u = dequeue Q
        For each v in adj(u)
            if v is in Q and v.key > f(u,v)
                v.key = f(u,v)
                v.parent = u

For Prim, pass f = w(u, v) and for Dijkstra pass f = u.key + w(u, v).

Another interesting thing is that above Generic can also implement Breadth First Search (BFS) although it would be overkill because expensive priority queue is not really required. To turn above Generic algorithm in to BFS, pass f = u.key + 1 which is same as enforcing all weights to 1 (i.e. BFS gives minimum number of edges required to traverse from point A to B).

Intuition

Here's one good way to think about above generic algorithm: We start with two buckets A and B. Initially, put all your vertices in B so the bucket A is empty. Then we move one vertex from B to A. Now look at all the edges from vertices in A that crosses over to the vertices in B. We chose the one edge using some criteria from these cross-over edges and move corresponding vertex from B to A. Repeat this process until B is empty.

A brute force way to implement this idea would be to maintain a priority queue of the edges for the vertices in A that crosses over to B. Obviously that would be troublesome if graph was not sparse. So question would be can we instead maintain priority queue of vertices? This in fact we can as our decision finally is which vertex to pick from B.

Historical Context

It's interesting that the generic version of the technique behind both algorithms is conceptually as old as 1930 even when electronic computers weren't around.

The story starts with Otakar Borůvka who needed an algorithm for a family friend trying to figure out how to connect cities in the country of Moravia (now part of the Czech Republic) with minimal cost electric lines. He published his algorithm in 1926 in a mathematics related journal, as Computer Science didn't existed then. This came to the attention to Vojtěch Jarník who thought of an improvement on Borůvka's algorithm and published it in 1930. He in fact discovered the same algorithm that we now know as Prim's algorithm who re-discovered it in 1957.

Independent of all these, in 1956 Dijkstra needed to write a program to demonstrate the capabilities of a new computer his institute had developed. He thought it would be cool to have computer find connections to travel between two cities of the Netherlands. He designed the algorithm in 20 minutes. He created a graph of 64 cities with some simplifications (because his computer was 6-bit) and wrote code for this 1956 computer. However he didn't published his algorithm because primarily there were no computer science journals and he thought this may not be very important. The next year he learned about the problem of connecting terminals of new computers such that the length of wires was minimized. He thought about this problem and re-discovered Jarník/Prim's algorithm which again uses the same technique as the shortest path algorithm he had discovered a year before. He mentioned that both of his algorithms were designed without using pen or paper. In 1959 he published both algorithms in a paper that is just 2 and a half page long.

Answer 4

Dijkstras algorithm is used only to find shortest path.

In Minimum Spanning tree(Prim's or Kruskal's algorithm) you get minimum egdes with minimum edge value.

For example:- Consider a situation where you wan't to create a huge network for which u will be requiring a large number of wires so these counting of wire can be done using Minimum Spanning Tree(Prim's or Kruskal's algorithm) (i.e it will give you minimum number of wires to create huge wired network connection with minimum cost).

Whereas "Dijkstras algorithm" will be used to get the shortest path between two nodes while connecting any nodes with each other.

Answer 5

Dijkstra finds the shortest path between it's beginning node and every other node. So in return you get the minimum distance tree from beginning node i.e. you can reach every other node as efficiently as possible.

Prims algorithm gets you the MST for a given graph i.e. a tree that connects all nodes while the sum of all costs is the minimum possible.

To make a story short with a realistic example:

1. Dijkstra wants to know the shortest path to each destination point by saving traveling time and fuel.
2. Prim wants to know how to efficiently deploy a train rail system i.e. saving material costs.

Answer 6

Dijkstra's algorithm doesn't create a MST, it finds the shortest path.

Consider this graph

       5     5
  s *-----*-----* t
     \         /
       -------
         9

The shortest path is 9, while the MST is a different 'path' at 10.