# Difference between revisions of "Shortest path"

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==Single-pair shortest path== | ==Single-pair shortest path== | ||

+ | We can compute a single-pair shortest path by taking the source <math>s</math> and running single-source shortest paths from it, and simply extracting the shortest path to the destination <math>t</math>. This computes a lot of extra information (namely, the shortest paths to all the other vertices as well), so we might wonder whether it is possible to do better. It turns out that there are no known single-pair shortest path algorithms that outperform single-source shortest paths algorithms ''in the worst case''. Nevertheless, it is often possible to do better in the ''average case''. | ||

+ | |||

+ | ===A* or heuristic search=== | ||

+ | In the single-pair shortest path problem, we know exactly where we want to go, we just don't know how to get there. But what if we were able to guess roughly how far away we are from the destination? Then we could proceed as in Dijkstra's algorithm, but give priority to exploring edges that we think take us closer to the destination rather than further away. Formally, we define a ''heuristic function'' <math>h:V \to \mathbb{R}</math> such that <math>h(v)</math> is a "guess" for the actual distance between <math>v</math> and <math>t</math> (the destination). If we can find such a heuristic function that never ''overestimates'' (only underestimates) the correct distance, we can use the [[A* search algorithm]] to take advantage of this information to help find a shortest path more quickly. (If <math>h</math> overestimates, then we may still get a relatively short path, but we are not guaranteed that it is a correct shortest path.) The better our estimates with <math>h</math>, the faster the search, but in the worst case, in which our heuristic is totally off, we cannot improve much over Dijkstra's. | ||

+ | |||

+ | ===Meet-in-the-middle=== | ||

+ | Another example of when we don't have to worry much about the worst case, and hence might be able to get a better search time, is when the graph we are searching is extremely large but yet we know that the shortest path is relatively short. For example, consider the Rubik's cube graph, in which each legal position is a vertex and an edge exists between two vertices if the positions they represent can be interconverted with one face turn. There are approximately <math>4.3\times10^19</math> positions of the Rubik's cube, but the shortest path between any pair of vertices always has length 20 or less.<ref>Tomas Rokicki ''et al.'' "God's Number is 20". Retrieved 2011-03-02 from http://cube20.org/</ref> The graph we are searching may even be infinite. | ||

==References== | ==References== | ||

+ | <references/> |

## Revision as of 22:50, 3 March 2011

The **shortest paths** problem is one of the most fundamental problems in graph theory. Given a directed graph , possibly weighted, and a set of pairs of vertices , the problem is to compute, for each , a **simple** path in from to (a list of vertices such that for all , ) such that no other simple path in from to has a lower total weight.

Shortest paths in undirected graphs can be computed by replacing each undirected edge with two arcs of the same weight, one going in each direction, to obtain a directed graph.

**Theorem**: In a graph with no cycles of negative weight, the shortest path is no shorter than the shortest simple path. (On the other hand, in a graph with a negative-weight cycle, lengths of paths may be unbounded below.)

**Proof**: We show that any path from to can be transformed into a simple path from to which is at least as short. Let the path be denoted . We proceed by induction on the number of pairs (with ) such that , which is countable. When there are zero such pairs, the path is already simple, and nothing needs to be done. Otherwise, we transform the path into another path with fewer such pairs but equal or lesser weight by removing the vertices from the path. The weight of the path therefore decreases by an amount equal to the weight of the cycle , which is nonnegative.

**Corollary**: Assuming our graphs have no cycles of negative weight, the restriction that the shortest paths be simple is immaterial. Therefore, we will assume in the foregoing discussion that our graphs have no cycles of negative weight, for the problem of finding shortest *simple* paths in graphs containing negative-weight cycles is NP-complete.

**Corollary**: In a finite connected graph, a shortest path always exists. (To prove this we simply use the fact that the graph has a finite number of simple paths, and only simple paths need be considered. So one of them must be the shortest.)

## Contents

## Variations

Three variations of the shortest path algorithm exist, and they are discussed in the following sections.

- In the
*single-pair shortest path*problem, there is only one pair in the problem set. In other words the shortest path is desired between a single pair of vertices. - In the
*single-source shortest paths*problem, the problem set is of the form . One vertex, , is designated the*source*, and we wish to find the shortest paths from the source to all other vertices. (To solve the analogous*single-destination shortest paths*problem, we merely reverse the directions of all edges, which reduces it to single-source.) - In the
*all-pairs shortest paths*problem, the problem set is ; that is, we wish to know the shortest paths from every vertex to every other vertex.

## Approach

All the shortest paths algorithms discussed in this article have the same basic approach. At their core, they compute not the shortest paths themselves, but the distances. Using information computed in order to compute the distances, one can easily then reconstruct the paths themselves. They begin with the knowledge that the distance from any vertex to itself is zero, and they overestimate all other distances they need. (By this it is meant that they find a number for each pair under consideration such that the distance from to is less than or equal to .) If , then the initial overestimate for the distance from to is the weight of the edge ; otherwise it is infinite. At some point, all overestimates will be refined, perhaps gradually, perhaps at once, so that once the algorithm has terminated, they are exactly the correct distances.

### Relaxation

There are theoretically many ways to refine overestimates but a specific way, known as **relaxation**, is used in all the algorithms discussed in this article. Relaxation can take place when three conditions are met:

- The currently best overestimate for the distance from some vertex to some vertex is ;
- The currently best overestimate for the distance from to some vertex is ,
- The currently best overestimate for the distance from to is greater than . (This includes the case in which it is infinite.)

Relaxation refines the best overestimate for the distance from to by setting it to , which is better than its current value.

**Theorem**: When the distances from to all other vertices are all overestimated, but no relaxations are possible, then those distances are all known correctly. Contrapositively, if at there exists such that the distance from to is not correctly known, then relaxation must be possible somewhere in the graph.

**Proof**: By induction on the number of edges in some shortest path from to (which we can take to be simple). It is vacuously true when this is zero or one, because all paths of length zero or one were accounted for in the initial overestimates. Assume the path has at least two edges, and denote by the last vertex on the path before . If the distance from to or from to is not known, then by the inductive hypothesis, we are done. Otherwise, notice that the path contains two subpaths, one from to and one from to (the latter is trivial as it consists of a single edge), and that each of these must itself be a shortest path, otherwise we could replace it with a shorter path to obtain a shorter path from to , a contradiction. Now, as the distances from to and to are correctly known, and the correct distance from to is exactly the sum of the distances from to and to (as these values equal the weights of the aforementioned subpaths), and our overestimate of the distance from to is incorrect, it must be strictly greater than the sum of the distances from to and to . Hence can be relaxed.

## Single-source shortest paths

## All-pairs shortest paths

## Single-pair shortest path

We can compute a single-pair shortest path by taking the source and running single-source shortest paths from it, and simply extracting the shortest path to the destination . This computes a lot of extra information (namely, the shortest paths to all the other vertices as well), so we might wonder whether it is possible to do better. It turns out that there are no known single-pair shortest path algorithms that outperform single-source shortest paths algorithms *in the worst case*. Nevertheless, it is often possible to do better in the *average case*.

### A* or heuristic search

In the single-pair shortest path problem, we know exactly where we want to go, we just don't know how to get there. But what if we were able to guess roughly how far away we are from the destination? Then we could proceed as in Dijkstra's algorithm, but give priority to exploring edges that we think take us closer to the destination rather than further away. Formally, we define a *heuristic function* such that is a "guess" for the actual distance between and (the destination). If we can find such a heuristic function that never *overestimates* (only underestimates) the correct distance, we can use the A* search algorithm to take advantage of this information to help find a shortest path more quickly. (If overestimates, then we may still get a relatively short path, but we are not guaranteed that it is a correct shortest path.) The better our estimates with , the faster the search, but in the worst case, in which our heuristic is totally off, we cannot improve much over Dijkstra's.

### Meet-in-the-middle

Another example of when we don't have to worry much about the worst case, and hence might be able to get a better search time, is when the graph we are searching is extremely large but yet we know that the shortest path is relatively short. For example, consider the Rubik's cube graph, in which each legal position is a vertex and an edge exists between two vertices if the positions they represent can be interconverted with one face turn. There are approximately positions of the Rubik's cube, but the shortest path between any pair of vertices always has length 20 or less.^{[1]} The graph we are searching may even be infinite.

## References

- ↑ Tomas Rokicki
*et al.*"God's Number is 20". Retrieved 2011-03-02 from http://cube20.org/