TSP – Infrastructure for the Traveling Salesperson Problem

The traveling salesperson problem (also known as traveling salesman problem or TSP) is a well known and important combinatorial optimization problem. The goal is to ﬁnd the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is diﬃcult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. Typical applications in operations research include vehicle routing, computer wiring, cutting wallpaper and job sequencing. The main application in statistics is combinatorial data analysis, e.g., reordering rows and columns of data matrices or identifying clusters. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to ﬁnd good solutions. In addition, it provides an interface to Concorde , one of the best exact TSP solvers currently available.


Introduction
The traveling salesperson problem (TSP; Lawler, Lenstra, Rinnooy Kan, and Shmoys 1985;Gutin and Punnen 2002) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Formally, the TSP can be stated as follows. The distances between n cities are stored in a distance matrix D with elements d ij where i, j = 1 . . . n and the diagonal elements d ii are zero. A tour can be represented by a cyclic permutation π of {1, 2, . . . , n} where π(i) represents the city that follows city i on the tour. The traveling salesperson problem is then the optimization problem to find a permutation π that minimizes the length of the tour denoted by n i=1 d iπ(i) . (1) For this minimization task, the tour length of (n − 1)! permutation vectors have to be compared. This results in a problem which is very hard to solve and in fact known to be NPcomplete (Johnson and Papadimitriou 1985a). However, solving TSPs is an important part of applications in many areas including vehicle routing, computer wiring, machine sequencing and scheduling, frequency assignment in communication networks (Lenstra and Kan 1975;Punnen 2002). Applications in statistical data analysis include ordering and clustering objects. For example, data analysis applications in psychology ranging from profile smoothing to finding an order in developmental data are presented by Hubert and Baker (1978). Clustering and ordering using TSP solvers is currently becoming popular in biostatistics. For example, Ray, Bandyopadhyay, and Pal (2007) describe an application for ordering genes and Johnson and Liu (2006) use a TSP solver for clustering proteins.
In this paper we give a very brief overview of the TSP and introduce the R package TSP which provides an infrastructure for handling and solving TSPs. The paper is organized as follows. In Section 2 we briefly present important aspects of the TSP including different problem formulations and approaches to solve TSPs. In Section 3 we give an overview of the infrastructure implemented in TSP and the basic usage. In Section 4, several examples are used to illustrate the package's capabilities. Section 5 concludes the paper.
A previous version of this manuscript was published in the Journal of Statistical Software (Hahsler and Hornik 2007).

Theory
In this section, we briefly summarize some aspects of the TSP which are important for the implementation of the TSP package described in this paper. For a complete treatment of all aspects of the TSP, we refer the interested reader to the classic book edited by Lawler et al. (1985) and the more modern book edited by Gutin and Punnen (2002).
It has to be noted that in this paper, following the origin of the TSP, the term distance is used. Distance is used here interchangeably with dissimilarity or cost and, unless explicitly stated, no restrictions to measures which obey the triangle inequality are made. An important distinction can be made between the symmetric TSP and the more general asymmetric TSP. For the symmetric case (normally referred to as just TSP ), for all distances in D the equality d ij = d ji holds, i.e., it does not matter if we travel from i to j or the other way round, the distance is the same. In the asymmetric case (called ATSP ), the distances are not equal for all pairs of cities. Problems of this kind arise when we do not deal with spatial distances between cities but, e.g., with the cost or necessary time associated with traveling between locations, where the price for the plane ticket between two cities may be different depending on which way we go.

Different formulations of the TSP
Other than the permutation problem in the introduction, the TSP can also be formulated as a graph theoretic problem. Here the TSP is formulated by means of a complete graph G = (V, E), where the cities correspond to the node set V = {1, 2, . . . , n} and each edge e i ∈ E has an associated weight w i representing the distance between the nodes it connects. If the graph is not complete, the missing edges can be replaced by edges with very large distances. The goal is to find a Hamiltonian cycle, i.e., a cycle which visits each node in the graph exactly once, with the least weight in the graph (Hoffman and Wolfe 1985). This formulation naturally leads to procedures involving minimum spanning trees for tour construction or edge exchanges to improve existing tours.
TSPs can also be represented as integer and linear programming problems (see, e.g., Punnen 2002). The integer programming (IP) formulation is based on the assignment problem with additional constraint of no sub-tours: n j=1 x ij = 1, i = 1, . . . , n, x ij = 0 or 1 no sub-tours allowed The solution matrix X = (x ij ) of the assignment problem represents a tour or a collection of sub-tour (several unconnected cycles) where only edges which corresponding to elements x ij = 1 are on the tour or a sub-tour. The additional restriction that no sub-tours are allowed (called sub-tour elimination constraints) restrict the solution to only proper tours. Unfortunately, the number of sub-tour elimination constraints grows exponentially with the number of cities which leads to an extremely hard problem.
The linear programming (LP) formulation of the TSP is given by: where m is the number of edges e i in G, w i ∈ w is the weight of edge e i and x is the incidence vector indicating the presence or absence of each edge in the tour. Again, the constraints given by x ∈ S are problematic since they have to contain the set of incidence vectors of all possible Hamiltonian cycles in G which amounts to a direct search of all (n − 1)! possibilities and thus in general is infeasible. However, relaxed versions of the linear programming problem with removed integrality and sub-tour elimination constraints are extensively used by modern TSP solvers where such a partial description of constraints is used and improved iteratively in a branch-and-bound approach.

Useful manipulations of the distance matrix
Sometimes it is useful to transform the distance matrix D = (d ij ) of a TSP into a different matrix D = (d ij ) which has the same optimal solution. Such a transformation requires that for any Hamiltonian cycle H in a graph represented by its distance matrix D the equality i,j∈H holds for suitable α > 0 and β ∈ R. From the equality we see that additive and multiplicative constants leave the optimal solution invariant. This property is useful to rescale distances, e.g., for many solvers, distances in the interval [0, 1] have to be converted into integers from 1 to a maximal value.
A different manipulation is to reformulate an asymmetric TSP as a symmetric TSP. This is possible by doubling the number of cities (Jonker and Volgenant 1983). For each city a dummy city is added. Between each city and its corresponding dummy city a very small value (e.g., −∞) is used. This makes sure that each city always occurs in the solution together with its dummy city. The original distances are used between the cities and the dummy cities, where each city is responsible for the distance going to the city and the dummy city is responsible for the distance coming from the city. The distances between all cities and the distances between all dummy cities are set to a very large value (e.g., ∞) which makes these edges infeasible. An example for equivalent formulations as an asymmetric TSP (to the left) and a symmetric TSP (to the right) for three cities is: Instead of the infinity values suitably large negative and positive values can be used. The new symmetric TSP can be solved using techniques for symmetric TSPs which are currently far more advanced than techniques for ATSPs. Removing the dummy cities from the resulting tour gives the solution for the original ATSP.

Finding exact solutions for the TSP
Finding the exact solution to a TSP with n cities requires to check (n − 1)! possible tours. To evaluate all possible tours is infeasible for even small TSP instances. To find the optimal tour Held and Karp (1962) presented the following dynamic programming formulation: Given a subset of city indices (excluding the first city) S ⊂ {2, 3, . . . , n} and l ∈ S, let d * (S, l) denote the length of the shortest path from city 1 to city l, visiting all cities in S in-between. For S = {l}, d * (S, l) is defined as d 1l . The shortest path for larger sets with |S| > 1 is Finally, the minimal tour length for a complete tour which includes returning to city 1 is d * * = min l∈{2,3,...,n} d * ({2, 3, . . . , n}, l) + d l1 . (3) Using the last two equations, the quantities d * (S, l) can be computed recursively and the minimal tour length d * * can be found. In a second step, the optimal permutation π = {1, i 2 , i 3 , . . . , i n } of city indices 1 through n can be computed in reverse order, starting with i n and working successively back to i 2 . The procedure exploits the fact that a permutation π can only be optimal, if and, for 2 ≤ p ≤ n − 1, The space complexity of storing the values for all d * (S, l) is (n−1)2 n−2 which severely restricts the dynamic programming algorithm to TSP problems of small sizes. However, for very small TSP instances this approach is fast and efficient.
A different method, which can deal with larger instances, uses a relaxation of the linear programming problem presented in Section 2.1 and iteratively tightens the relaxation till a solution is found. This general method for solving linear programming problems with complex and large inequality systems is called cutting plane method and was introduced by Dantzig, Fulkerson, and Johnson (1954).
Each iteration begins with using instead of the original linear inequality description S the relaxation Ax ≤ b, where the polyhedron P defined by the relaxation contains S and is bounded. The optimal solution x * of the relaxed problem can be obtained using standard linear programming solvers. If the x * found belongs to S, the optimal solution of the original problem is obtained, otherwise, a linear inequality can be found which is satisfied by all points in S but violated by x * . Such an inequality is called a cutting plane or cut. A family of such cutting planes can be added to the inequality system Ax ≤ b to obtain a tighter relaxation for the next iteration.
If no further cutting planes can be found or the improvement in the objective function due to adding cuts gets very small, the problem is branched into two sub-problems which can be minimized separately. Branching is done iteratively which leads to a binary tree of subproblems. Each sub-problem is either solved without further branching or is found to be irrelevant because its relaxed version already produces a longer path than a solution of another sub-problem. This method is called branch-and-cut (Padberg and Rinaldi 1990) which is a variation of the well known branch-and-bound (Land and Doig 1960) procedure.
The initial polyhedron P used by Dantzig et al. (1954) contains all vectors x for which all x e ∈ x satisfy 0 ≤ x e ≤ 1 and in the resulting tour each city is linked to exactly two other cities. Various separation algorithms for finding subsequent cuts to prevent sub-tours (subtour elimination inequalities) and to ensure an integer solution (Gomory cuts;Gomory 1963) were developed over time. The currently most efficient implementation of this method is Concorde described in Applegate, Bixby, Chvátal, and Cook (2000).

Heuristics for the TSP
The NP-completeness of the TSP already makes it more time efficient for small-to-medium size TSP instances to rely on heuristics in case a good but not necessarily optimal solution is sufficient. TSP heuristics typically fall into two groups, tour construction heuristics which create tours from scratch and tour improvement heuristics which use simple local search heuristics to improve existing tours.
In the following we will only discuss heuristics available in TSP, for a comprehensive overview of the multitude of TSP heuristics including an experimental comparison, we refer the reader to the book chapter by Johnson and McGeoch (2002).

Tour construction heuristics
The implemented tour construction heuristics are the nearest neighbor algorithm and the insertion algorithms.
Nearest neighbor algorithm. The nearest neighbor algorithm (Rosenkrantz, Stearns, and Philip M. Lewis 1977) follows a very simple greedy procedure: The algorithm starts with a tour containing a randomly chosen city and then always adds to the last city in the tour the nearest not yet visited city. The algorithm stops when all cities are on the tour.
An extension to this algorithm is to repeat it with each city as the starting point and then return the best tour found. This heuristic is called repetitive nearest neighbor.
Insertion algorithms. All insertion algorithms (Rosenkrantz et al. 1977) start with a tour consisting of an arbitrary city and then choose in each step a city k not yet on the tour. This city is inserted into the existing tour between two consecutive cities i and j, such that the insertion cost (i.e., the increase in the tour's length) is minimized. The algorithms stop when all cities are on the tour.
The insertion algorithms differ in the way the city to be inserted next is chosen. The following variations are implemented: Nearest insertion The city k is chosen in each step as the city which is nearest to a city on the tour.

Farthest insertion
The city k is chosen in each step as the city which is farthest from any of the cities on the tour.

Cheapest insertion
The city k is chosen in each step such that the cost of inserting the new city is minimal.

Arbitrary insertion
The city k is chosen randomly from all cities not yet on the tour.
The nearest and cheapest insertion algorithms correspond to the minimum spanning tree algorithm by Prim (1957). Adding a city to a partial tour corresponds to adding an edge to a partial spanning tree. For TSPs with distances obeying the triangular inequality, the equality to minimum spanning trees provides a theoretical upper bound for the two algorithms of twice the optimal tour length.
The idea behind the farthest insertion algorithm is to link cities far outside into the tour first to establish an outline of the whole tour early. With this change, the algorithm cannot be directly related to generating a minimum spanning tree and thus the upper bound stated above cannot be guaranteed. However, it can was shown that the algorithm generates tours which approach 2/3 times the optimal tour length (Johnson and Papadimitriou 1985b).

Tour improvement heuristics
Tour improvement heuristics are simple local search heuristics which try to improve an initial tour. A comprehensive treatment of the topic can be found in the book chapter by Rego and Glover (2002).
k-Opt heuristics. The idea is to define a neighborhood structure on the set of all admissible tours. Typically, a tour t is a neighbor of another tour t if t can be obtained from t by deleting k edges and replacing them by a set of different feasible edges (a k-Opt move). In such a structure, the tour can iteratively be improved by always moving from one tour to its best neighbor till no further improvement is possible. The resulting tour represents a local optimum which is called k-optimal.
Lin-Kernighan heuristic. This heuristic (Lin and Kernighan 1973) does not use a fixed value for k for its k-Opt moves, but tries to find the best choice of k for each move. The heuristic uses the fact that each k-Opt move can be represented as a sequence of 2-Opt moves. It builds up a sequence of 2-Opt moves, checking after each additional move whether a stopping rule is met. Then the part of the sequence which gives the best improvement is used. This is equivalent to a choice of one k-Opt move with variable k. Such moves are used till a local optimum is reached.
By using full backtracking, the optimal solution can always be found, but the running time would be immense. Therefore, only limited backtracking is allowed in the procedure, which helps to find better local optima or even the optimal solution. Further improvements to the procedure are described by Lin and Kernighan (1973).

Computational infrastructure: the TSP package
In package TSP, a traveling salesperson problem is defined by an object of class TSP (symmetric) or ATSP (asymmetric). solve_TSP() is used to find a solution, which is represented by an object of class TOUR. Figure 1 gives an overview of this infrastructure.
TSP objects can be created from a distance matrix (a dist object) or a symmetric matrix using the creator function TSP() or coercion with as.TSP(). Similarly, ATSP objects are created by ATSP() or as.ATSP() from square matrices representing the distances. In the creation process, labels are taken and stored as city names in the object or can be explicitly given as arguments to the creator functions. Several methods are defined for the classes: print() displays basic information about the problem (number of cities and the distance measure employed).
n_of_cities() returns the number of cities. labels() returns the city names.
image() produces a shaded matrix plot of the distances between cities. The order of the cities can be specified as the argument order.
Internally, an object of class TSP is a dist object with an additional class attribute and, therefore, if needed, can be coerced to dist or to a matrix. An ATSP object is represented as a square matrix. Obviously, asymmetric TSPs are more general than symmetric TSPs, hence, symmetric TSPs can also be represented as asymmetric TSPs. To formulate an asymmetric TSP as a symmetric TSP with double the number of cities (see Section 2.2), reformulate_ATSP_as_TSP() is provided. This function creates the necessary dummy cities and adapts the distance matrix accordingly.
A popular format to save TSP descriptions to disk which is supported by most TSP solvers is the format used by TSPLIB, a library of sample instances of the TSP maintained by Reinelt (2004). The TSP package provides read_TSPLIB() and write_TSPLIB() to read and save symmetric and asymmetric TSPs in TSPLIB format.
Class TOUR represents a solution to a TSP by an integer permutation vector containing the ordered indices and labels of the cities to visit. In addition, it stores an attribute indicating the length of the tour. Again, suitable print() and labels() methods are provided. The raw permutation vector (i.e., the order in which cities are visited) can be obtained from a tour using as.integer(). With cut_tour(), a circular tour can be split at a specified city resulting in a path represented by a vector of city indices.
The length of a tour can always be calculated using tour_length() and specifying a TSP and a tour. Instead of the tour, an integer permutation vector calculated outside the TSP package can be used as long as it has the correct length.
All TSP solvers in TSP can be used with the simple common interface: solve_TSP (x, method, control) where x is the TSP to be solved, method is a character string indicating the method used to solve the TSP and control can contain a list with additional information used by the solver. The available algorithms are shown in Table 1.
All algorithms except the Concorde TSP solver and the Chained Lin-Kernighan heuristic (a Lin-Kernighan variation described in Applegate, Cook, and Rohe (2003)) are included in the package and distributed under the GNU Public License (GPL). For the Concorde TSP solver and the Chained Lin-Kernighan heuristic only a simple interface (using write_TSPLIB(), calling the executable and reading back the resulting tour) is included in TSP. The executable itself is part of the Concorde distribution, has to be installed separately and is governed by a different license which allows only for academic use. The interfaces are included since Concorde (Applegate et al. 2000;Applegate, Bixby, Chvátal, and Cook 2006) is currently one of the best implementations for solving symmetric TSPs based on the branch-and-cut method discussed in section 2.3. In May 2004, Concorde was used to find the optimal solution for the TSP of visiting all 24,978 cities in Sweden. The computation was carried out on a cluster with 96 Xeon 2.8 GHz nodes and took in total almost 100 CPU years.

Examples
In this section we provide some examples for the use of package TSP. We start with a simple example of how to use the interface of the TSP solver to compare different heuristics. Then we show how to solve related tasks, using the Hamiltonian shortest path problem as an example. Finally, we give an example of clustering using the TSP package. An additional application can be found in package seriation (Hahsler, Buchta, and Hornik 2006) which uses the TSP solvers from TSP to order (seriate) objects given a proximity matrix.

Comparing some heuristics
In the following example, we use several heuristics to find a short path in the USCA50 data set which contains the distances between the first 50 cities in the USCA312 data set. The USCA312 data set contains the distances between 312 cities in the USA and Canada coded as a symmetric TSP. The smaller data set is used here, since some of the heuristic solvers employed are rather slow.
R> library("TSP") R> data("USCA50") R> USCA50 object of class 'TSP' 50 cities (distance 'euclidean') We calculate tours using different heuristics and store the results in the list tours. As an example, we show the first tour which displays the method employed, the number of cities involved and the tour length. All tour lengths are compared using the dot chart in Figure 2.
For the chart, we add a point for the optimal solution which has a tour length of 14497. The optimal solution can be found using Concorde (method = "concorde"). It is omitted here, since Concorde has to be installed separately.

Finding the shortest Hamiltonian path
The problem of finding the shortest Hamiltonian path through a graph (i.e., a path which visits each node in the graph exactly once) can be transformed into the TSP with cities and distances representing the graphs vertices and edge weights, respectively (Garfinkel 1985).
Finding the shortest Hamiltonian path through all cities disregarding the endpoints can be achieved by inserting a 'dummy city' which has a distance of zero to all other cities. The position of this city in the final tour represents the cutting point for the path. In the following we use a heuristic to find a short path in the USCA312 data set. Inserting dummy cities is performed in TSP by insert_dummy(). R> library("TSP") R> data("USCA312") R> tsp <-insert_dummy(USCA312, label = "cut") R> tsp object of class 'TSP' 313 cities (distance 'euclidean') The TSP now contains an additional dummy city and we can try to solve this TSP.
R> tour <-solve_TSP(tsp, method="farthest_insertion") R> tour object of class 'TOUR' result of method 'farthest_insertion' for 313 cities tour length: 39333 Since the dummy city has distance zero to all other cities, the path length is equal to the tour length reported above. The path starts with the first city in the list after the 'dummy' city and ends with the city right before it. We use cut_tour() to create a path and show the first and last 6 cities on it.
R> path <-cut_tour(tour, "cut") R> head(labels(path)) [1] "Eureka, CA" "Eugene, OR" "Salem, OR" "Portland, OR" [5] "Yakima, WA" "Tacoma, WA" R> tail(labels(path)) [1] "San Francisco, CA" "San Jose, CA" "Santa Cruz, CA" [4] "Hilo, HI" "Honolulu, HI" "Lihue, HI" The tour found in the example results in a path from Lihue on Hawaii to Prince Rupert in British Columbia. Such a tour can also be visualized using the packages sp, maps and maptools (Pebesma and Bivand 2005). Note that he if statement and the plot in the else part in the following is not needed and only checks if the appropriate packages are installed when building this document.
For the following two examples, we indicate how the distance matrix between cities can be modified to solve related shortest Hamiltonian path problems. These examples serve as illustrations of how modifications can be made to transform different problems into a TSP.
The first problem is to find the shortest Hamiltonian path starting with a given city. In this case, all distances to the selected city are set to zero, forcing the evaluation of all possible paths starting with this city and disregarding the way back from the final city in the tour. By modifying the distances the symmetric TSP is changed into an asymmetric TSP (ATSP) since the distances between the starting city and all other cities are no longer symmetric.
As an example, we choose New York as the starting city. We transform the data set into an ATSP and set the column corresponding to New York to zero before solving it. Thus, the distance to return from the last city in the path to New York does not contribute to the path length. We use the nearest neighbor heuristic to calculate an initial tour which is then improved using 2-Opt moves and cut at New York to create a path.

R> plot_path(path)
The found path is presented in Figure 4. It begins with New York and cities in New Jersey and ends in a city in Manitoba, Canada.
Concorde and many advanced TSP solvers can only solve symmetric TSPs. To use these solvers, we can formulate the ATSP as a TSP using reformulate_ATSP_as_TSP() which introduces a dummy city for each city (see Section 2.2). After finding a tour for the TSP, the dummy cities are removed again giving the tour for the original ATSP. Note that the tour needs to be reversed if the dummy cities appear before and not after the original cities in the solution of the TSP. The following code is not executed here, since it takes several minutes to execute and Concorde has to be installed separately. Concorde finds the optimal solution with a length of 36091.
R> tour <-solve_TSP(tsp, method = "concorde") R> tour <-as.TOUR(tour[tour <= n_of_cities(atsp)]) Finding the shortest Hamiltonian path which ends in a given city can be achieved likewise by setting the row in the distance matrix which corresponds to this city to zero.
For finding the shortest Hamiltonian path we can also restrict both end points. This problem can be transformed to a TSP by replacing the two cities by a single city which contains the distances from the start point in the columns and the distances to the end point in the rows.
Obviously this is again an asymmetric TSP.
For the following example, we are only interested in paths starting in New York and ending in Los Angeles. Therefore, we remove the two cities from the distance matrix, create an asymmetric TSP and insert a dummy city called "LA/NY". The distances from this dummy city are replaced by the distances from New York and the distances towards are replaced by the distances towards Los Angeles.

R> plot_path(path_ids)
The path jumps from New York to cities in Ontario and it passes through cities in California and Nevada before ending in Los Angeles. The path displayed in Figure 5 contains multiple crossings which indicate that the solution is suboptimal. The optimal solution generated by reformulating the problem as a TSP and using Concorde has only a tour length of 38489.

Rearrangement clustering
Solving a TSP to obtain a clustering was suggested several times in the literature (see, e.g., Lenstra 1974;Alpert and Kahng 1997;Johnson, Krishnan, Chhugani, Kumar, and Venkatasubramanian 2004). The idea is that objects in clusters are visited in consecutive order and Figure 5: A Hamiltonian path for the USCA312 dataset starting in New York and ending in Los Angles.
from one cluster to the next larger "jumps" are necessary. Climer and Zhang (2006) call this type of clustering rearrangement clustering and suggest to automatically find the cluster boundaries of k clusters by adding k dummy cities which have constant distance c to all other cities and are infinitely far from each other. In the optimal solution of the TSP, the dummy cities must separate the most distant cities and thus represent optimal boundaries for k clusters.
For the following example, we use the well known iris data set. Since we know that the dataset contains three classes denoted by the variable Species, we insert three dummy cities into the TSP for the iris data set and perform rearrangement clustering using the default method (nearest insertion algorithm). Note that this algorithm does not find the optimal solution and it is not guaranteed that the dummy cities will present the best cluster boundaries.

R> ## plot the distance matrix
One pair of red horizontal and vertical lines exactly separates the darker from lighter areas. The second pair occurs inside the larger dark block. We can look at how well the partitioning obtained fits the structure in the data given by the species field in the data set. Since we used the species as the city labels in the TSP, the labels in the tour represent the partitioning with the dummy cities named 'boundary' separating groups. The result can be summarized based on the run length encoding of the obtained tour labels: R> out <-rle(labels(tour)) R> data.frame(Species = out$values, + Lenghts = out$lengths, + Pos = cumsum(out$lengths)) Even in the optimal tour found by Concorde, this problem occurs. The reason why the rearrangement clustering fails to split the data into three groups is the closeness between the groups 'Virginica' and 'Versicolor'. To inspect this problem further, we can project the data points on the first two principal components of the data set and add the path segments which resulted from solving the TSP.
R> prc <-prcomp(iris[1:4]) R> plot(prc$x, pch = as.numeric(iris[,5]), col = as.numeric(iris[,5])) R> paths <-cut_tour(tour, cut = "boundary") R> for(p in paths) lines(prc$x[p, ]) The result in shown in Figure 7. The three species are identified by different markers and all points connected by a single path represent a cluster found. Clearly, the two groups to the right side of the plot are too close to be separated correctly by using just the distances between individual points. This problem is similar to the chaining effect known from hierarchical clustering using the single-linkage method.

Conclusion
In this paper we presented the R extension package TSP which implements an infrastructure to handle and solve TSPs. The package introduces classes for problem descriptions (TSP and ATSP) and for the solution (TOUR). Together with a simple interface for solving TSPs, it allows for an easy and transparent usage of the package.
With the interface to Concorde, TSP also can use a state of the art implementation which efficiently computes exact solutions using branch-and-cut.