elkai is a Python 3 library for solving travelling salesman problems without external dependencies, based on LKH by Keld Helsgaun.

💾 To install it run pip install elkai

💻 Supported platforms: elkai is available on Windows, Linux, OS X for Python 3.5 and above as a binary wheel.

import numpy as np
import elkai

M = np.zeros((3, 3), dtype=int)
M[0, 1] = 4
M[1, 2] = 5

print(elkai.solve_int_matrix(M))
# Output: [0, 2, 1]

elkai.solve_int_matrix(matrix, runs=10):

• matrix: an N*N matrix representing an integer distance matrix between cities.

  • N=3 example: (can be any indexed structure: list, numpy array etc)

  [                  # cities are zero indexed, d() is distance
      [0, 4,  9],    # d(0, 0), d(0, 1), d(0, 2)
      [4, 0, 10],    # d(1, 0), d(1, 1), ...
      [2, 4,  0]     # ... and so on
  ]

• runs: An integer representing how many iterations the solver should perform. By default, 10.

• Returns: The tour represented as a list of indices. The indices are zero-indexed and based on the distance matrix order.

elkai.solve_float_matrix(matrix, runs=10) has the same signature as the previous, but allows floating point distances. It may be inaccurate.

How to manually build the library?

You need CMake, a C compiler and Python 3.5+. You need to install the dev dependencies first: pip install scikit-build ninja. To build and install, run python setup.py install.

How accurately does it solve asymmetric TSP problems?

Instances with known solutions, which are up to N=315 cities, can be solved optimally.

What's the difference between LKH and elkai?

elkai packages the C LKH code into a nicer C library and then wraps it and compiles it into a Python wheel. Note: Dr. Helsgaun has released the LKH project for non-commercial use only, so elkai as a derivative work must be used this way too.

Does it lock the GIL during a run?

Yes.