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## Shortest Paths

Every path in a weighted digraph has an associated path weight, the value of which is the sum of the weights of that path's edges. This essential measure allows us to formulate such problems as "find the lowest-weight path between two given vertices." These shortest-paths problems are the topic of this chapter. Not only are shortest-paths problems intuitive for many direct apps, but they also take us into a powerful and general realm where we seek efficient algorithms to solve general problems that can encompass a broad variety of specific apps. Several of the algorithms that we consider in this chapter relate directly to various algorithms that we examined in s 17 through 20. Our basic graph-search paradigm applies immediately, and several of the specific mechanisms that we used in s 17 and 19 to address connectivity in graphs and digraphs provide the basis for us to solve shortest-paths problems. For economy, we refer to weighted digraphs as networks. Screenshot shows a sample network, with standard representations. We have already developed an ADT interface with adjacency-matrix and adjacency-lists class implementations for networks in —we just pass true as a second parameter when we call the constructor so that the class keeps one representation of each edge, precisely as we did when deriving digraph representations in from the undirected graph representations in (see Programs 20.1 through 20.4).

##### Screenshot Sample network and representations

This network (weighted digraph) is shown in four representations: list of edges, drawing, adjacency matrix, and adjacency lists (left to right). As we did for MST algorithms, we show the weights in matrix entries and in list nodes, but use edge references in our programs. While we often use edge weights that are proportional to their lengths in the drawing (as we did for MST algorithms), we do not insist on this rule because most shortest-paths algorithms handle arbitrary nonnegative weights (negative weights do present special challenges). The adjacency matrix is not symmetric, and the adjacency lists contain one node for each edge (as in unweighted digraphs). Nonexistent edges are represented by null references in the matrix (blank in the figure) and are not present at all in the lists. Self-loops of length 0 are present because they simplify our implementations of shortest-paths algorithms. They are omitted from the list of edges at left for economy and to indicate the typical scenario where we add them by convention when we create an adjacency-matrix or adjacency-lists representation.

As discussed at length in , we use references to abstract edges for weighted digraphs to broaden the applicability of our implementations. This approach has certain implications that are different for digraphs than the ones that we considered for undirected graphs in and are worth noting. First, since there is only one representation of each edge, we do not need to use the from method in the edge class (see Program 20.1) when using an iterator: In a digraph, e.from(v) is true for every edge reference e returned by an iterator for v. Second, as we saw in , it is often useful when processing a digraph to be able to work with its reverse graph, but we need a different approach than that taken by Program 19.1, because that implementation creates edges to create the reverse, and we assume that a graph ADT whose clients provide references to edges should not create edges on its own (see Exercise 21.3). In apps or systems for which we need all types of graphs, it is a textbook exercise in software engineering to define a network ADT from which ADTs for the unweighted undirected graphs of s 17 and 18, the unweighted digraphs of , or the weighted undirected graphs of can be derived (see Exercise 21.10). When we work with networks, it is generally convenient to keep self-loops in all the representations. This convention allows algorithms the flexibility to use a sentinel maximum-value weight to indicate that a vertex cannot be reached from itself. In our examples, we use self-loops of weight 0, although positive-weight self-loops certainly make sense in many apps. Many apps also call for parallel edges, perhaps with differing weights. As we mentioned in , various options for ignoring or combining such edges are appropriate in various different apps. In this chapter, for simplicity, none of our examples use parallel edges, and we do not allow parallel edges in the adjacency-matrix representation; we also do not check for parallel edges or remove them in adjacency lists. All the connectivity properties of digraphs that we considered in are relevant in networks. In that chapter, we wished to know whether it is possible to get from one vertex to another; in this chapter, we take weights into consideration—we wish to find the best way to get from one vertex to another. Definition 21.1 A shortest path between two vertices s and t in a network is a directed simple path from s to t with the property that no other such path has a lower weight. This definition is succinct, but its brevity masks points worth examining. First, if t is not reachable from s, there is no path at all, and therefore there is no shortest path. For convenience, the algorithms that we consider often treat this case as equivalent to one in which there exists an infinite-weight path from s to t. Second, as we did for MST algorithms, we use networks where edge weights are proportional to edge lengths in examples, but the definition has no such requirement and our algorithms (other than the one in ) do not make this assumption. Indeed, shortest-paths algorithms are at their best when they discover counterintuitive shortcuts, such as a path between two vertices that passes through several other vertices but has total weight smaller than that of a direct edge connecting those vertices. Third, there may be multiple paths of the same weight from one vertex to another; we typically are content to find one of them. Screenshot shows an example with general weights that illustrates these points.

##### Screenshot Shortest-path trees

A shortest-path tree (SPT) defines shortest paths from the root to other vertices (see Definition 21.2). In general, different paths may have the same length, so there may be multiple SPTs defining the shortest paths from a given vertex. In the example network shown at left, all shortest paths from 0 are subgraphs of the DAG shown to the right of the network. A tree rooted at 0 spans this DAG if and only if it is an SPT for 0. The two trees at right are such trees.

The restriction in the definition to simple paths is unnecessary in networks that contain edges that have nonnegative weight, because any cycle in a path in such a network can be removed to give a path that is no longer (and is shorter unless the cycle comprises zero-weight edges). But when we consider networks with edges that could have negative weight, the need for the restriction to simple paths is readily apparent: Otherwise, the concept of a shortest path is meaningless if there is a cycle in the network that has negative weight. For example, suppose that the edge 3-5 in the network in Screenshot were to have weight -.38, and edge 5-1 were to have weight -.31. Then, the weight of the cycle 1-4-3-5-1 would be .32 + .36 - .38 - .31 = -.01, and we could spin around that cycle to generate arbitrarily short paths. Note carefully that, as is true in this example, it is not necessary for all the edges on a negative-weight cycle to be of negative weight; what counts is the sum of the edge weights. For brevity, we use the term negative cycle to refer to directed cycles whose total weight is negative. In the definition, suppose that some vertex on a path from s to t is also on a negative cycle. In this case, the existence of a (nonsimple) shortest path from s to t would be a contradiction, because we could use the cycle to construct a path that had a weight lower than any given value. To avoid this contradiction, we restrict to simple paths in the definition so that the concept of a shortest path is well defined in any network. However, we do not consider negative cycles in networks until , because, as we see there, they present a truly fundamental barrier to the solution of shortest-paths problems. To find shortest paths in a weighted undirected graph, we build a network with the same vertices and with two edges (one in each direction) corresponding to each edge in the graph. There is a one-to-one correspondence between simple paths in the network and simple paths in the graph, and the costs of the paths are the same; so shortest-paths problems are equivalent. Indeed, we build precisely such a network when we build the standard adjacency-lists or adjacency-matrix representation of a weighted undirected graph (see, for example, Screenshot). This construction is not helpful if weights can be negative, because it gives negative cycles in the network, and we do not know how to solve shortest-paths problems in networks that have negative cycles (see ). Otherwise, the algorithms for networks that we consider in this chapter also work for weighted undirected graphs. In certain apps, it is convenient to have weights on vertices instead of, or in addition to, weights on edges; and we might also consider more complicated problems where both the number of edges on the path and the overall weight of the path play a role. We can handle such problems by recasting them in terms of edge-weighted networks (see, for example, Exercise 21.4) or by slightly extending the basic algorithms (see, for example, Exercise 21.52). Because the distinction is clear from the context, we do not introduce special terminology to distinguish shortest paths in weighted graphs from shortest paths in graphs that have no weights (where a path's weight is simply its number of edges—see ). The usual nomenclature refers to (edge-weighted) networks, as used in this chapter, since the special cases presented by undirected or unweighted graphs are handled easily by algorithms that process networks. We are interested in the same basic problems that we defined for undirected and unweighted graphs in . We restate them here, noting that Definition 21.1 implicitly generalizes them to take weights into account in networks. Source–sink shortest path Given a start vertex s and a finish vertex t, find a shortest path in the graph from s to t. We refer to the start vertex as the source and to the finish vertex as the sink, except in contexts where this usage conflicts with the definition of sources (vertices with no incoming edges) and sinks (vertices with no outgoing edges) in digraphs. Single-source shortest paths Given a start vertex s, find shortest paths from s to each other vertex in the graph. All-pairs shortest paths Find shortest paths connecting each pair of vertices in the graph. For brevity, we sometimes use the term all shortest paths to refer to this set of V2 paths. If there are multiple shortest paths connecting any given pair of vertices, we are content to find any one of them. Since paths have varying number of edges, our implementations provide methods that allow clients to trace paths in time proportional to the paths' lengths. Any shortest path also implicitly gives us the shortest-path length, but our implementations explicitly provide lengths. In summary, to be precise, when we say "find a shortest path" in the problem statements just given, we mean "compute the shortest-path length and a way to trace a specific path in time proportional to that path's length." Screenshot illustrates shortest paths for the example network in Screenshot. In networks with V vertices, we need to specify V paths to solve the single-source problem and to specify V2 paths to solve the all-pairs problem. In our implementations, we use a representation more compact than these lists of paths; we first noted it in , and we consider it in detail in .

##### Screenshot All shortest paths

This table gives all the shortest paths in the network of Screenshot and their lengths. This network is strongly connected, so there exist paths connecting each pair of vertices. The goal of a source-sink shortest-path algorithm is to compute one of the entries in this table; the goal of a single-source shortest-paths algorithm is to compute one of the rows in this table; and the goal of an all-pairs shortest-paths algorithm is to compute the whole table. Generally, we use more compact representations, which contain essentially the same information and allow clients to trace any path in time proportional to its number of edges (see Screenshot).

In Java implementations, we build our algorithmic solutions to these problems into ADT implementations that allow us to build efficient client programs that can solve a variety of practical graph-processing problems. For example, as we see in , we implement solutions to the all-pairs shortest-paths classes as constructors within classes that support constant-time shortest-path queries. We also build classes to solve single-source problems so that clients who need to compute shortest paths from a specific vertex (or a small set of them) can avoid the expense of computing shortest paths for other vertices. Careful consideration of such issues and proper use of the algorithms that we examine can mean the difference between an efficient solution to a practical problem and a solution that is so costly that no client could afford to use it. Shortest-paths problems arise in various guises in numerous apps. Many of the apps appeal immediately to geometric intuition, but many others involve arbitrary cost structures. As we did with minimum spanning trees (MSTs) in , we sometimes take advantage of geometric intuition to help develop an understanding of algorithms that solve the problems but stay cognizant that our algorithms operate properly in more general settings. In , we do consider specialized algorithms for Euclidean networks. More important, in s 21.6 and 21.7, we see that the basic algorithms are effective for numerous apps where networks represent an abstract model of the computation. Road maps Tables that give distances between all pairs of major cities are a prominent feature of many road maps. We presume that the map maker took the trouble to be sure that the distances are the shortest ones, but our assumption is not necessarily always valid (see, for example, Exercise 21.11). Generally, such tables are for undirected graphs that we should treat as networks with edges in both directions corresponding to each road, though we might contemplate handling one-way streets for city maps and some similar apps. As we see in , it is not difficult to provide other useful information, such as a table that tells how to execute the shortest paths (see Screenshot). In modern apps, embedded systems provide this kind of capability in cars and transportation systems. Maps are Euclidean graphs; in , we examine shortest-paths algorithms that take into account the vertex position when they seek shortest paths.

##### Screenshot Distances and paths

Road maps typically contain distance tables like the one in the center for this tiny subset of French cities connected by highways as shown in the graph at the top. Though rarely found in maps, a table like the one at the bottom would also be useful, as it tells what signs to follow to execute the shortest path. For example, to decide how to get from Paris to Nice, we can check the table, which says to begin by following signs to Lyon.

Airline routes Route maps and schedules for airlines or other transportation systems can be represented as networks for which various shortest-paths problems are of direct importance. For example, we might wish to minimize the time that it takes to fly between two cities or to minimize the cost of the trip. Costs in such networks might involve functions of time, of money, or of other complicated resources. For example, flights between two cities typically take more time in one direction than the other because of prevailing winds. Air travelers also know that the fare is not necessarily a simple function of the distance between the cities—situations where it is cheaper to use a circuitous route (or endure a stopover) than to take a direct flight are all too common. Such complications can be handled by the basic shortest-paths algorithms that we consider in this chapter; these algorithms are designed to handle any positive costs. The fundamental shortest-paths computations suggested by these apps only scratch the surface of the applicability of shortest-paths algorithms. In , we consider problems from apps areas that appear unrelated to these natural ones, in the context of a discussion of reduction, a formal mechanism for proving relationships among problems. We solve problems for these apps by transforming them into abstract shortest-paths problems that do not have the intuitive geometric feel of the problems just described. Indeed, some apps lead us to consider shortest-paths problems in networks with negative weights. Such problems can be far more difficult to solve than are problems where negative weights cannot occur. Shortest-paths problems for such apps not only bridge a gap between elementary algorithms and unsolved algorithmic challenges but also lead us to powerful and general problem-solving mechanisms. As with MST algorithms in , we often mix the weight, cost, and distance metaphors. Again, we normally exploit the natural appeal of geometric intuition even when working in more general settings with arbitrary edge weights; thus we refer to the "length" of paths and edges when we should say "weight" and to one path as "shorter" than another when we should say that it "has lower weight." We also might say that v is "closer" to s than w when we should say that "the lowest-weight directed path from s to v has weight lower than that of the lowest-weight directed path s to w," and so forth. This usage is inherent in the standard use of the term "shortest paths" and is natural even when weights are not related to distances (see Screenshot); however, when we expand our algorithms to handle negative weights in , we must abandon such usage. This chapter is organized as follows: After introducing the basic underlying principles in , we introduce basic algorithms for the single-source and all-pairs shortest-paths problems in s 21.2 and 21.3. Then, we consider acyclic networks (or, in a clash of shorthand terms, weighted DAGs) in and ways of exploiting geometric properties for the source–sink problem in Euclidean graphs in . We then cast off in the other direction to look at more general problems in s 21.6 and 21.7, where we explore shortest-paths algorithms, perhaps involving networks with negative weights, as a high-level problem-solving tool.

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