Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Malinga Perera, profile picture
Uploaded byMalinga Perera
8,051 views

Floyd warshall-algorithm

The Floyd-Warshall algorithm finds the shortest paths between all pairs of vertices in a weighted graph. It works by computing the shortest path between all pairs where intermediate vertices are restricted to being from a subset at each step, until the subset is the full vertex set. It has O(V^3) time complexity and O(V^2) space complexity. The algorithm stores predecessor pointers to extract the actual shortest paths between pairs of vertices once the distances are computed.

Education◦Technology◦
Downloaded 210 times
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    
The All-Pairs Shortest Paths Problem © ¨¦¤¢ § ¥ £ ¡   Given a weighted digraph with a weight function , where is the set of real numbers, 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 12 d 20 6 e 3 5 3 4 17 b a 8 c without negative cost cycle e 4 b −20 4 5 4 d 10 c with negative cost cycle 2
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 sophisticated data structures.   ¢ ©   ¢ ¦ ¤ §¥£ © ¡ ¨ © ¢   ¢   ¡ Solution 2: Assume no negative cycles. Dynamic programming solution, based on a natural decomposition of the problem. . using “ repeated squaring”. ©   ¦ ¤ ¥£ ¢   ¢ ¡ ©   ¢ ¡ This lecture: Assume no negative cycles. develop another dynamic programming algorithm, the . Floyd-Warshall algorithm, with time complexity Also illustrates that there can be more than one way of developing a dynamic programming algorithm. © ¢   3 ¢ ¡
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:     § © ¥ ¢ © ¥ ¢ ! ¤£¡ ¤ ¡ ¡ ¡ ¡ ¥¦ ¥ ¢ © ¦¨ § © ¡ ¤£¢ ¡ Output Format: an distance is the distance from vertex to . § ¥ and and   if if if , . where 4       ¤£¡
Step 1: The Floyd-Warshall Decomposition are called the . £   ¥ ¨ ¨ ¨ ¥ ¥ ¡   §   ¡ § ¥£ ¨¦¤  ¥ ¨ ¨ ¨ © ¥ ¥ Definition: The vertices intermediate vertices of the path ¢   ¢  ¡ Let be the length of the shortest path from to such that all intermediate vertices on the path (if any) are in set . ¨ ¨ ¥ ¨ # $¥ ! , i.e., no intermediate vertex. . matrix ! 0¤£ ¡   ¤£¢ ¡     is the distance from to . So our aim 4 4 4 565¥ ! ¥ © ¡ % for 5 ¥ 1 32 ¤£¡ Subproblems: compute   . ¨ 1 32 ¤£¡   is to compute ) ¤ Claim: be the ' % (¥   ¤£¡ Let is set to be  
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 contains 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 , there are two possibilities: from the set ' (% ¥ . £ ¨¥ 0 § ¡ ¤£¡ ¡ ¨¥ § ¨ ¨ ¥ # $¥ ! % % 2. is a vertex on the path. The shortest such path has length ¨¥ § ¨ 1. is not a vertex on the path, The shortest such path has length 6 .
Step 2: Structure of shortest paths Consider a shortest path from to containing the vertex . It consists of a subpath from to and a subpath from to . Each subpath can only contain intermediate vertices , and must be as short as possible, in namely they have lengths and . % % ¨¥ § % ' ¡ ¨¥ 0 § £ ¨¥ § £ ¡ ¡ ¨¥ § % $¥ ¨ ¨ ¨ ¥ !   ! Hence the path has length . Combining the two cases we get ¨ ¦ 7 § ¥ 0 £ ¡ ¡ ¨¥ § ¥ ¨¥ § ¤£¡ ¥ £ ¤¢ ¡ ¡ ¤£¡
8 ¢ £ ¨¥ § ¡ ¡ ¤£¡ § ¥ 0 ¥ ¨¥ § ¨¥ § .   ¥ £ ¢ ¨ ¨ ¨ ¥ ! ¤£¡ ) ¤ ¡ ¡ % ¡ ¡ ¡ from ! ¤£¡ for Compute   Bottom: using , the weight matrix.   Step 3: the Bottom-up Computation
9 ! ¥ % ¥ ¨¥ § ¨¥ § © ! ! § ¥ ¨¥ ! § § % % ¥ ¨¥ 0 ¥ % § ¥ 0 ¡ ! ! ! ! ¥ ¡ ¥ ! ¤© £ ¡ ¡ ¥ ! ¡ ¥ 0 ! % ¥ ¨¥   § ; ; ; ! ¡     ¨ ¨ ¨ ¨ 1 § return else ¨ for to do for to do for to do if ; !   ¡ !   !   dynamic programming ¥ ¦ ! ¥ ; ;   ¡ ¡ % ! ¤¢© § £ ¡ !¥ ¡ ¥ )   ! ¡ ¡   Floyd-Warshall( ) for to do for to do !   initialize     ¡ ¥ The Floyd-Warshall Algorithm: Version 1
Comments on the Floyd-Warshall Algorithm © ¢ ¢   The algorithm’s running time is clearly .   ¨ ¥ ©   The predecessor pointer can be used to extract the final path (see later ). ! © ¢ ¢ © ¡   ¢         ¦ ¤ ¢ §¥£¡ Problem: the algorithm uses space. It is possible to reduce this down to space by keeping only one matrix instead of . Algorithm is on next page. Convince yourself that it works. 10    
! ! ! ¥ ! £ ¡ ¤© ! ! § % ¡ ¥ ! ¥ % ! % ¥ © ¥ ! ¥ % ¡ ¥ ! ¥   ¢ %     ¨ ¨ ¨ ¨ § return 11 ; ; ¡ for to do for to do for to do if ; © ! ¡   !   ¡ !   dynamic programming ; ; ¡ ¡ % § ¥ ¦ ! ¤¢© £ ¡ ! ¥ ¡ ¡ ¥ ! ¥   !     Floyd-Warshall( ) for to do for to do !   initialize   ¡   ¡ ¥ The Floyd-Warshall Algorithm: Version 2
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 and the shortest path from from to to . 12 ! ¥ £ ¡ ¤¢©
The Algorithm for Extracting the Shortest Paths ¥ ¦   ¥ ! ¢ ¥ ©   if ( ) ¡ ¥ Path( ) single edge £ ¡ ¤¢© output ; compute the two parts of the path else ! £ © ¡ ! ¥ ¥ £ ¡ ¥ ¤¢© ¥   Path( Path( ); ); 13 § §
14 (4,6) (6,3) (2,5) (5,4) © © ¡ © © ¡ ¥¦ ¥ ¦ ¥¦ ¥ ¦ ¡ ¡     ¡ ! ! ! ¡     ¥ ¥ ! ¡ ¡ ¦ ¥   ! ¡   ¡ ¦ ¥ ¡ ¤ ! ¡ ¥ # ¥ ¥ © ! ¡ ¥ # ¥ ¡ ¤ ¡ ¡ # ¥   £¤¢© ¡ £¤¢© ¡ £¤¢© ¡ £¤¢© ¡ £¤¢© ¡ £¤¢© ¡ £ ¡ ¤¢© © ©   ¥ ¥ ¢ ¨ ¢ © ¡ ¥ ¢   © ¡ ¥ ¢ ¡ £ ¥ ¤ © ¨¥ #$¢ ¥ © ¡£¥ #$¢ # $¢ © ¥   Path Path Path Path Path Path Path     ¨ ¨ # ¡ ¥ ! #   ¨ ¨ ¡ ¥ ¦¨ ¨ # ¡ ¥ #   ¨ ¨ ¡   ¨ ¨ ¥ ¡ ¢¨ ¨   ¨ ¨ # ¥ ¡ §¨ ¨ # ¥ ¦¨ ¨   ¨ ¨ # ¡ ¢¨ ¨ #   ¨ ¨ Find the shortest path from vertex 2 to vertex 3. Example of Extracting the Shortest Paths