“Floyd–Warshall algorithm”的意思、由来-开放百科全书

In computer science, the Floyd–Warshall algorithm is an algorithm for finding shortest paths in a weighted graph with positive or negative edge weights (but with no negative cycles).^[1]^[2] A single execution of the algorithm will find the lengths (summed weights) of shortest paths between all pairs of vertices. Although it does not return details of the paths themselves, it is possible to reconstruct the paths with simple modifications to the algorithm.

Versions of the algorithm can also be used for finding the transitive closure of a relation

, or (in connection with the Schulze voting system) widest paths between all pairs of vertices in a weighted graph.

History and naming

The Floyd–Warshall algorithm is an example of dynamic programming, and was published in its currently recognized form by Robert Floyd in 1962.^[3] However, it is essentially the same as algorithms previously published by Bernard Roy in 1959^[4] and also by Stephen Warshall in 1962^[5] for finding the transitive closure of a graph,^[6] and is closely related to Kleene's algorithm (published in 1956) for converting a deterministic finite automaton into a regular expression.^[7] The modern formulation of the algorithm as three nested for-loops was first described by Peter Ingerman, also in 1962.^[8]

The algorithm is also known as Floyd's algorithm, the Roy–Warshall algorithm, the Roy–Floyd algorithm, or the WFI algorithm.

Algorithm

The Floyd–Warshall algorithm compares all possible paths through the graph between each pair of vertices. It is able to do this with

comparisons in a graph. This is remarkable considering that there may be up to

edges in the graph, and every combination of edges is tested. It does so by incrementally improving an estimate on the shortest path between two vertices, until the estimate is optimal.

Consider a graph

with vertices

numbered 1 through

. Further consider a function

that returns the shortest possible path from

using vertices only from the set

as intermediate points along the way. Now, given this function, our goal is to find the shortest path from each

to each

using only vertices in

We know that the best path from

that only uses vertices

through

is defined by

, and it is clear that if there were a better path from

, then the length of this path would be the concatenation of the shortest path from

(only using intermediate vertices in

) and the shortest path from

(only using intermediate vertices in

is the weight of the edge between vertices

and

, we can define

in terms of the following recursive formula: the base case is

This formula is the heart of the Floyd–Warshall algorithm. The algorithm works by first computing

for all

pairs for

, then

, etc. This process continues until

, and we have found the shortest path for all

pairs using any intermediate vertices. Pseudocode for this basic version follows:

Example

Prior to the first iteration of the outer loop, labeled {{math|1=k = 0}} above, the only known paths correspond to the single edges in the graph. At {{math|1=k = 1}}, paths that go through the vertex 1 are found: in particular, the path [2,1,3] is found, replacing the path [2,3] which has fewer edges but is longer (in terms of weight). At {{math|1=k = 2}}, paths going through the vertices {1,2} are found. The red and blue boxes show how the path [4,2,1,3] is assembled from the two known paths [4,2] and [2,1,3] encountered in previous iterations, with 2 in the intersection. The path [4,2,3] is not considered, because [2,1,3] is the shortest path encountered so far from 2 to 3. At {{math|1=k = 3}}, paths going through the vertices {1,2,3} are found. Finally, at {{math|1=k = 4}}, all shortest paths are found.

The distance matrix at each iteration of {{mvar|k}}, with the updated distances in bold, will be:

Behavior with negative cycles

A negative cycle is a cycle whose edges sum to a negative value. There is no shortest path between any pair of vertices

which form part of a negative cycle, because path-lengths from

can be arbitrarily small (negative). For numerically meaningful output, the Floyd–Warshall algorithm assumes that there are no negative cycles. Nevertheless, if there are negative cycles, the Floyd–Warshall algorithm can be used to detect them. The intuition is as follows:

Hence, to detect negative cycles using the Floyd–Warshall algorithm, one can inspect the diagonal of the path matrix, and the presence of a negative number indicates that the graph contains at least one negative cycle.^[9] To avoid numerical problems one should check for negative numbers on the diagonal of the path matrix within the inner for loop of the algorithm.^[10] Obviously, in an undirected graph a negative edge creates a negative cycle (i.e., a closed walk) involving its incident vertices. Considering all edges of the above example graph as undirected, e.g. the vertex sequence 4 – 2 – 4 is a cycle with weight sum −2.

Path reconstruction

The Floyd–Warshall algorithm typically only provides the lengths of the paths between all pairs of vertices. With simple modifications, it is possible to create a method to reconstruct the actual path between any two endpoint vertices. While one may be inclined to store the actual path from each vertex to each other vertex, this is not necessary, and in fact, is very costly in terms of memory. Instead, the shortest-path tree can be calculated for each node in

time using

memory to store each tree which allows us to efficiently reconstruct a path from any two connected vertices.

Pseudocode ^[11] :

Analysis

and compute the sequence of

matrices

, the total number of operations used is

Applications and generalizations

The Floyd–Warshall algorithm can be used to solve the following problems, among others:

Implementations

Comparison with other shortest path algorithms

The Floyd–Warshall algorithm is a good choice for computing paths between all pairs of vertices in dense graphs, in which most or all pairs of vertices are connected by edges. For sparse graphs with non-negative edge weights, a better choice is to use Dijkstra's algorithm from each possible starting vertex, since the running time of repeated Dijkstra (

using Fibonacci heaps) is better than the

running time of the Floyd–Warshall algorithm when

is significantly smaller than

. For sparse graphs with negative edges but no negative cycles, Johnson's algorithm can be used, with the same asymptotic running time as the repeated Dijkstra approach.

There are also known algorithms using fast matrix multiplication to speed up all-pairs shortest path computation in dense graphs, but these typically make extra assumptions on the edge weights (such as requiring them to be small integers).^[14]^[15] In addition, because of the high constant factors in their running time, they would only provide a speedup over the Floyd–Warshall algorithm for very large graphs.

References

1. ^{{Introduction to Algorithms|1}} See in particular Section 26.2, "The Floyd–Warshall algorithm", pp. 558–565 and Section 26.4, "A general framework for solving path problems in directed graphs", pp. 570–576.
2. ^{{cite book | author=Kenneth H. Rosen | title=Discrete Mathematics and Its Applications, 5th Edition | publisher = Addison Wesley | year=2003 | isbn=978-0-07-119881-3 }}
3. ^{{cite journal | first = Robert W. | last = Floyd | authorlink = Robert W. Floyd | title = Algorithm 97: Shortest Path | journal = Communications of the ACM | volume = 5 | issue = 6 | page = 345 | date= June 1962 | doi = 10.1145/367766.368168 }}
4. ^{{cite journal | first = Bernard | last = Roy |authorlink=Bernard Roy| title = Transitivité et connexité. | journal = C. R. Acad. Sci. Paris | volume = 249 | pages = 216–218 | year= 1959 }}
5. ^{{cite journal | first = Stephen | last = Warshall | title = A theorem on Boolean matrices | journal = Journal of the ACM | volume = 9 | issue = 1 | pages = 11–12 | date= January 1962 | doi = 10.1145/321105.321107 }}
6. ^{{mathworld|id=Floyd-WarshallAlgorithm | title = Floyd-Warshall Algorithm}}
7. ^{{cite book | authorlink = Stephen Cole Kleene | first = S. C. | last = Kleene | chapter = Representation of events in nerve nets and finite automata | title = Automata Studies | editor = C. E. Shannon and J. McCarthy | pages = 3–42 | publisher = Princeton University Press | year= 1956 }}
8. ^{{cite journal | first = Peter Z. | last = Ingerman | title = Algorithm 141: Path Matrix | journal = Communications of the ACM | volume = 5 | number = 11 | page = 556 | date = November 1962 | doi = 10.1145/368996.369016 }}
9. ^{{cite web | first = Dorit | last = Hochbaum | authorlink = Dorit S. Hochbaum | url = http://www.ieor.berkeley.edu/~hochbaum/files/ieor266-2014.pdf | title = Section 8.9: Floyd-Warshall algorithm for all pairs shortest paths | work = Lecture Notes for IEOR 266: Graph Algorithms and Network Flows | date = 2014 | format = PDF | publisher = Department of Industrial Engineering and Operations Research, University of California, Berkeley}}
10. ^{{cite journal | title = The Floyd–Warshall algorithm on graphs with negative cycles | author = Stefan Hougardy | journal = Information Processing Letters | url = http://www.sciencedirect.com/science/article/pii/S002001901000027X | volume = 110 | number = 8-9 | date = April 2010 | pages = 279–281 | doi=10.1016/j.ipl.2010.02.001}}
11. ^https://books.goalkicker.com/AlgorithmsBook/
12. ^{{citation|title=Handbook of Graph Theory|series=Discrete Mathematics and Its Applications|first1=Jonathan L.|last1=Gross|first2=Jay|last2=Yellen|publisher=CRC Press|year=2003|page=65|url=https://books.google.com/books?id=mKkIGIea_BkC&pg=PA65|isbn=9780203490204}}.
13. ^{{cite journal | title = Algebraic Structures for Transitive Closure | first = Rafael | last = Penaloza|citeseerx = 10.1.1.71.7650}}
14. ^{{citation | last = Zwick | first = Uri | authorlink = Uri Zwick | date = May 2002 | doi = 10.1145/567112.567114 | issue = 3 | journal = Journal of the ACM | pages = 289–317 | title = All pairs shortest paths using bridging sets and rectangular matrix multiplication | volume = 49| arxiv = cs/0008011}}.
15. ^{{citation | last = Chan | first = Timothy M. | authorlink = Timothy M. Chan | date = January 2010 | doi = 10.1137/08071990x | issue = 5 | journal = SIAM Journal on Computing | pages = 2075–2089 | title = More algorithms for all-pairs shortest paths in weighted graphs | volume = 39| citeseerx = 10.1.1.153.6864 }}.