README.md

The Traveling Salesman Problem: Computational Limits

This repository contains implementations for analyzing the Traveling Salesman Problem (TSP) and understanding computational limits.

Setup

1. Clone this repository
2. Navigate to the repository directory
3. Generate the test datasets:

   python location_generator.py

This creates a data/ directory with emergency site datasets of various sizes (5, 8, 10, 12, 15, 20 locations).

Running the Code

The assignment asks you to run four different commands to analyze the algorithms:

1. Test Small Cases (Verify Correctness)

python tsp_solver.py --test-small

Tests both brute force and nearest neighbor on small datasets to verify they produce correct results.

2. Time Brute Force Algorithm

python tsp_solver.py --time-brute-force

Times the brute force algorithm on datasets of size 5, 8, 10, 12, and 15. Warning: Larger sizes may take several minutes or timeout after 60 seconds.

3. Time Approximation Algorithm

python tsp_solver.py --time-approximation

Times the nearest neighbor approximation on datasets of size 5, 8, 10, 12, 15, and 20.

4. Compare Both Algorithms

python tsp_solver.py --compare-all

Compares brute force and approximation side-by-side on small datasets where both can complete.

File Structure

.
├── README.md                  # This file
├── location_generator.py      # Generates test datasets
├── tsp_solver.py             # TSP algorithm implementations and timing utilities
└── data/                     # Generated test datasets
    ├── sites_5.json
    ├── sites_8.json
    ├── sites_10.json
    ├── sites_12.json
    ├── sites_15.json
    └── sites_20.json

Need Help?

If you get an error about missing data files, make sure you've run:

python location_generator.py

If you need to see all available options:

python tsp_solver.py --help