# Instantiating a TSP problem¶

Here we give two examples on how to instantiate a TSP problem from a weights matrix or a TSPLIB XML file http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/

## From weights¶

The weights matrix is a two dimensional vector of doubles, e.g.

weights = [[0, 1, 2], [3, 0, 5], [6, 7, 0]]


PyGMO ignores main diagonal elements. Notice in the example that the diagonal elements are zero.

In the first example we create a list of list (matrix) and print the instantiated tsp problem to console:

from PyGMO.problem import tsp

# creating a list of lists
list2D = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

# creating an instance of a TSP problem
tsp_instance = tsp(list2D)

# printing the TSP instance to console
print tsp_instance


And the resulting TSP problem looks like this. Again, notice there are no connections between a vertex and itself, regardless of what we put in the main diagonal, in our case the values 0, 4 and 8 are ignored.

Problem name: Traveling Salesman Problem
Global dimension:                       6
Integer dimension:                      6
Fitness dimension:                      1
Constraints dimension:                  8
Inequality constraints dimension:       2
Lower bounds: [0, 0, 0]
Upper bounds: [1, 1, 1]
Constraints tolerance: []
Vertices = { 0 1 2 }
Edges (Source, Target) = Weight :
(0, 1) = 1
(0, 2) = 2
(1, 0) = 3
(1, 2) = 5
(2, 0) = 6
(2, 1) = 7


Global dimension $$= n*n-n = n*(n-1) = 3 * 2 = 6$$

Integer dimension $$= n*(n-1) = 3 * 2 = 6$$

Fitness dimension (max) $$= 1 \in [0,1]$$

Constraints dimension (global) = Equality + Inequality $$= 2*n + (n-1)*(n-2) = n*(n-1)+2 = 3 * 2 + 2 = 8$$

Equality constraints dimension $$= 2*n$$

Inequality constraints dimension $$= (n-1)*(n-2) = 2 * 1 = 2$$

## From TSPLIB XML¶

In this second example we will be loading an TSPLIB XML file from the current folder (pwd in linux).

In order to load an XML file, we use the utility function PyGMO.util.tsp.read_tsplib(‘file.xml’) which returns a weights matrix (list of list) such as the one defined above.

from PyGMO.util import tsp as tsputil
from PyGMO.problem import tsp

# importing the XML file

# printing the weights matrix
tsputil.print_matrix(weights)

# creating a tsp problem from the imported weights matrix
tsp_instance = tsp(weights)

# printing the tsp problem details to console
print tsp_instance


The imported matrix, notice it is symmetric.

[[    0.   153.   510.   706.   966.   581.   455.    70.   160.   372.   157.   567.   342.   398.]
[  153.     0.   422.   664.   997.   598.   507.   197.   311.   479.   310.   581.   417.   376.]
[  510.   422.     0.   289.   744.   390.   437.   491.   645.   880.   618.   374.   455.   211.]
[  706.   664.   289.     0.   491.   265.   410.   664.   804.  1070.   768.   259.   499.   310.]
[  966.   997.   744.   491.     0.   400.   514.   902.   990.  1261.   947.   418.   635.   636.]
[  581.   598.   390.   265.   400.     0.   168.   522.   634.   910.   593.    19.   284.   239.]
[  455.   507.   437.   410.   514.   168.     0.   389.   482.   757.   439.   163.   124.   232.]
[   70.   197.   491.   664.   902.   522.   389.     0.   154.   406.   133.   508.   273.   355.]
[  160.   311.   645.   804.   990.   634.   482.   154.     0.   276.    43.   623.   358.   498.]
[  372.   479.   880.  1070.  1261.   910.   757.   406.   276.     0.   318.   898.   633.   761.]
[  157.   310.   618.   768.   947.   593.   439.   133.    43.   318.     0.   582.   315.   464.]
[  567.   581.   374.   259.   418.    19.   163.   508.   623.   898.   582.     0.   275.   221.]
[  342.   417.   455.   499.   635.   284.   124.   273.   358.   633.   315.   275.     0.   247.]
[  398.   376.   211.   310.   636.   239.   232.   355.   498.   761.   464.   221.   247.     0.]]


And finally, the output for printing the TSP problem instance:

Problem name: Traveling Salesman Problem
Global dimension:                       182
Integer dimension:                      182
Fitness dimension:                      1
Constraints dimension:                  184
Inequality constraints dimension:       156
Lower bounds: [0, 0, ..., 0]
Upper bounds: [1, 1, ..., 1]
Constraints tolerance: [0, 0, ..., 0]