msaw97 / TSP

My work on Travelling Salesman Problem and its approximation algorithms.

TSP

My work on Travelling Salesman Problem and its approximation algorithms.

Usage

Main block solves randomly generated TSP instance with reference to provided CLI arguments

main.py N [-h] [-b] [-nn] [-ci] [-rnn] [-hk] [-EDM] [-eps EPS]

• obligatory argument N sets number the of vertices of a graph;
• -b solves generated TSP instance with brute force algorithm;
• -nn solves generated TSP instance with nearest neighbor algorithm;
• -ci solves generated TSP instance with cheapest insertion algorithm;
• -rnn solves generated TSP instance with repetitive nearest neighbor algorithm;
• -hk solves generated TSP instance with accurate Held-Karp algorithm;
• -EDM sets generated graph edges to satisfy the triangular inequality;
• -eps sets some given scalar EPS which multiplies all of the graph edges weights.

Other included files

- time_experiment.py analyzes time it takes to compute each of the implemented algorithms;

- error_experiment.py analyzes relative error of approximation algorithms;

- test_algorithms.py contains unit tests for algorithms.py module.

Time and error analysis results