https://github.com/nschloe/tspsolve

Solution algorithms for the travelling salesman problem.
https://github.com/nschloe/tspsolve

Last synced: 16 days ago
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Solution algorithms for the travelling salesman problem.

• Host: GitHub
• URL: https://github.com/nschloe/tspsolve
• Owner: nschloe
• Created: 2018-07-09T12:30:03.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-07-09T15:50:42.000Z (almost 7 years ago)
• Last Synced: 2025-06-07T08:08:17.128Z (27 days ago)
• Language: Python
• Size: 12.7 KB
• Stars: 1
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# tspsolve

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Algorithms for the [traveling salesman problem

(TSP)](https://en.wikipedia.org/wiki/Travelling_salesman_problem) in Python.

Implemented so far:

  * Nearest neighbor algorithm

    ```python

    import tspsolve

    # Create matrix of distances d

    path = tspsolve.nearest_neighbor(d)

    ```

  * [2-opt](https://en.wikipedia.org/wiki/2-opt) improvement

    ```python

    import tspsolve

    # Create matrix of distances d and an initial path

    new_path = tspsolve.two_opt(d, path, verbose=True)

    ```

For [Euclidiean

TSP](https://en.wikipedia.org/wiki/Travelling_salesman_problem#Euclidean_TSP), the

distance matrix can be computed efficiently with

```python

dx = numpy.subtract.outer(x, x)

dy = numpy.subtract.outer(y, y)

d = numpy.sqrt(dx ** 2 + dy ** 2)

```

### Installation

tspsolve is [available from the Python Package

Index](https://pypi.org/project/tspsolve/), so simply type

```

pip install -U tspsolve

```

to install or upgrade.

### Testing

To run the tspsolve unit tests, check out this repository and type

```

pytest

```

### Distribution

To create a new release

1. bump the `__version__` number,

2. publish to PyPi and GitHub:

    ```

    make publish

    ```

### License

tspsolve is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).