Metadata-Version: 2.1
Name: tspsolve
Version: 0.1.0
Summary: Python project scaffold
Home-page: https://github.com/nschloe/tspsolve
Author: Nico Schlömer
Author-email: nico.schloemer@gmail.com
License: License :: OSI Approved :: MIT License
Description: # tspsolve
        
        [![CircleCI](https://img.shields.io/circleci/project/github/nschloe/tspsolve/master.svg)](https://circleci.com/gh/nschloe/tspsolve/tree/master)
        [![codecov](https://img.shields.io/codecov/c/github/nschloe/tspsolve.svg)](https://codecov.io/gh/nschloe/tspsolve)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        [![PyPi Version](https://img.shields.io/pypi/v/tspsolve.svg)](https://pypi.org/project/tspsolve)
        [![GitHub stars](https://img.shields.io/github/stars/nschloe/tspsolve.svg?logo=github&label=Stars)](https://github.com/nschloe/tspsolve)
        
        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).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
