tsp_greedy, a Python code which reads a file of city-to-city distances, and solves a small traveling salesperson problem (TSP) using the greedy algorithm. It picks a starting city at random, and then successively visits the nearest unvisited city.

The user must prepare a file beforehand, containing the city-to-city distances. The program will request the name of this file, and then read it in as a matrix d. An example of such a file is:

        0  3  4  2  7
        3  0  4  6  3
        4  4  0  5  8
        2  6  5  0  6
        7  3  8  6  0
The distance file d should be square, symmetric, and have a zero diagonal.

A tour of n cities can be represented as a permutation p on the integers 0 through n-1. The cost of the tour, that is, the length, is the sum

        cost = sum ( 0 <= i < n ) ( d(p(i),p(i+1)) )
where p(n) is understood to mean p(0).

The greedy algorithm starts at one of the cities, and then successively moves to the nearest unvisited city, producing a tour. The tour may depend on the starting city, and so all n cities are tried. At the end, the shortest observed tour is reported.


The computer code and data files described and made available on this web page are distributed under the MIT license


tsp_greedy is available in a MATLAB version and a Python version.

Related Data and Programs:

change_making, a Python code which considers the change making problem, in which a given sum is to be formed using coins of various denominations.

partition_problem, a Python code which seeks solutions of the partition problem, splitting a set of integers into two subsets with equal sum.

satisfy, a Python code which demonstrates, for a particular circuit, an exhaustive search for solutions of the circuit satisfiability problem.

subset_sum, a Python code which seeks solutions of the subset sum problem.

tsp, a dataset directory which contains test data for the traveling salesperson problem;

tsp_brute, a Python code which reads a file of city-to-city distances and solves the traveling salesperson problem, using brute force.


  1. Gerhard Reinelt,
    TSPLIB - A Traveling Salesman Problem Library,
    ORSA Journal on Computing,
    Volume 3, Number 4, Fall 1991, pages 376-384.

Source Code:

Last revised on 06 March 2022.