The Floyd–Warshall algorithm
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The document describes the Floyd-Warshall algorithm for finding shortest paths in a weighted graph. It discusses the all-pairs shortest path problem, previous solutions using Dijkstra's algorithm and dynamic programming, and then presents the Floyd-Warshall algorithm as another dynamic programming solution. The algorithm works by computing the shortest path between all pairs of vertices where intermediate vertices are restricted to a given set. It does this using a bottom-up computation in O(V^3) time and O(V^2) space.
The Floyd–Warshall algorithm
- 1. Lecture 15: The Floyd-Warshall Algorithm CLRS section 25.2 Outline of this Lecture Recalling the all-pairs shortest path problem. Recalling the previous two solutions. The Floyd-Warshall Algorithm. 1
- 2. The All-Pairs Shortest Paths Problem Given a weighted digraph with a weight function , where is the set of real num- bers, determine the length of the shortest path (i.e., distance ) between all pairs of vertices in . Here we assume that there are no cycle with zero or negative cost. a b c d 20 12 5 17 8 − 20 5 10 4 4 4 a b c d e without negative cost cycle 6 e 3 3 4 with negative cost cycle 2
- 3. Solutions Covered in the Previous Lecture Solution 1: Assume no negative edges . Run Dijkstra’s algorithm, times, once with each vertex as source. with more sophisticateddata structures. Solution 2: Assume no negative cycles . Dynamic programming solution, based on a nat- ural decomposition of the problem. . using “ repeated squaring”. This lecture: Assume no negative cycles . develop another dynamic programming algorithm, the Floyd-Warshallalgorithm, with time complexity . Also illustrates that there can be more than one way of developing a dynamic programming algorithm. 3
- 4. Solution 3: the Input and Output Format As in the previous dynamic programming algorithm, we assume that the graph is represented by an matrix with the weights of the edges: if if if , . Output Format: an and and distance where is the distance from vertex to . 4
- 5. Step 1: The Floyd-Warshall Decomposition Definition: The vertices intermediate vertices of the path are called the . Let be the length of the shortest path from to such that all intermediate vertices on the path ( if any ) are in set is set to be . , i.e., no intermediate vertex. Let be the matrix . Claim: is the distance from to . So our aim is to compute . Subproblems: compute for 5
- 6. Step 2: Structure of shortest paths Observation 1: A shortest path does not contain the same vertex twice. Proof: A path containing the same vertex twice con- tains a cycle. Removing cycle gives a shorter path. Observation 2: For a shortest path from to such that any intermediate vertices on the path are chosen from the set , there are two possibilities: 1. is not a vertex on the path, The shortest such path has length . 2. is a vertex on the path. The shortest such path has length . 6
- 7. Step 2: Structure of shortest paths Consider a shortest path from to containing the to and a vertex . It consists of a subpath from subpath from to . Each subpath can only contain intermediate vertices in , and must be as short as possible, and . namely they have lengths Hence the path has length . Combining the two cases we get 7
- 8. Step 3: the Bottom-up Computation Bottom: , the weight matrix. using Compute for from . 8
- 9. The Floyd-Warshall Algorithm: Version 1 Floyd-Warshall( ) to do initialize for for to do ; ; for to do dynamic programming to do for for to do if ; ; else ; return ; 9
- 10. Comments on the Floyd-Warshall Algorithm The algorithm’s running time is clearly . The predecessor pointer to extract the final path (see later ). Problem: the algorithm uses It is possible to reduce this down to can be used space. space by keeping only one matrix instead of . Algorithm is on next page. Convince yourself that it works. 10
- 11. The Floyd-Warshall Algorithm: Version 2 Floyd-Warshall( ) to do initialize for for to do ; ; for to do dynamic programming to do for for to do if ; ; return ; 11
- 12. Extracting the Shortest Paths The predecessor pointers can be used to extract the final path. The idea is as follows. Whenever we discover that the shortest path from to passes through an intermediate vertex , we set . If the shortest path does not pass through any inter- mediate vertex, then . . To find the shortest path from to , we consult If it is nil, then the shortest path is just the edge . Otherwise, we recursively compute the shortest path from to and the shortest path from to . 12
- 13. The Algorithm for Extracting the Shortest Paths Path( ) if ( ) single edge output else Path( Path( ; compute the two parts of the path ); ); 13
- 14. Example of Extracting the Shortest Paths Find the shortest path from vertex 2 to vertex 3. Path Path Path Path Path Path Path (2,5) (5,4) (4,6) (6,3) 14