lap is a linear assignment problem solver using Jonker-Volgenant algorithm for dense (LAPJV [1]) or sparse (LAPMOD [2]) matrices.

Both algorithms are implemented from scratch based solely on the papers [1,2] and the public domain Pascal implementation provided by A. Volgenant [3].

In my tests the LAPMOD implementation seems to be faster than the LAPJV implementation for matrices with a side of more than ~5000 and with less than 50% finite coefficients.

[1] R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems", Computing 38, 325-340 (1987)
[2] A. Volgenant, "Linear and Semi-Assignment Problems: A Core Oriented Approach", Computer Ops Res. 23, 917-932 (1996)
[3] http://www.assignmentproblems.com/LAPJV.htm

Running lap requires:

• Python (2.7, 3.7, 3.8, 3.9)
• NumPy (>=1.10.1)

In addition to above, running the tests requires:

• SciPy, pytest, pytest-timeout

You can install the latest release of lap from PyPI (recommended):

pip install lap

Alternatively, you can install lap directly from the repository:

pip install git+git://github.com/gatagat/lap.git

In some cases installing from PyPI results in building the package, in these cases make sure to check "Install from source" below.

1. Install a C++ compiler (e.g., g++)

2. Python headers (e.g., python-dev package on Debian/Ubuntu)

3. Install Cython (>=0.21)

4. Clone

   git clone https://github.com/gatagat/lap.git
   

5. Under the root of the repo

   python setup.py build
   python setup.py install
   

On Windows, the build may fail if there is a mismatch between the MSVC compiler version used for the build and the version used to build Python itself. For more information see this stackoverflow answer.

Tested under Linux, OS X, Windows.

cost, x, y = lap.lapjv(C)

The function lapjv(C) returns the assignment cost (cost) and two arrays, x, y. If cost matrix C has shape N x M, then x is a size-N array specifying to which column is row is assigned, and y is a size-M array specifying to which row each column is assigned. For example, an output of x = [1, 0] indicates that row 0 is assigned to column 1 and row 1 is assigned to column 0. Similarly, an output of x = [2, 1, 0] indicates that row 0 is assigned to column 2, row 1 is assigned to column 1, and row 2 is assigned to column 0.

Note that this function does not return the assignment matrix (as done by scipy's linear_sum_assignment and lapsolver's solve dense). The assignment matrix can be constructed from x as follows:

A = np.zeros((N, M))
for i in range(N):
    A[i, x[i]] = 1

Equivalently, we could construct the assignment matrix from y:

A = np.zeros((N, M))
for j in range(M):
    A[y[j], j] = 1

Finally, note that the outputs are redundant: we can construct x from y, and vise versa:

x = [np.where(y == i)[0][0] for i in range(N)]
y = [np.where(x == j)[0][0] for j in range(M)]

Released under the 2-clause BSD license, see LICENSE.

Copyright (C) 2012-2017, Tomas Kazmar