Linear Assignment Problem solver (LAPJV/LAPMOD).
lapx
basically is Tomas Kazmar's gatagat/lap
with support for all Windows/Linux/macOS and Python 3.7/3.8/3.9/3.10/3.11/3.12.
LICENSE
@gatagat
Install from PyPI:
pip install lapx
Or install from GitHub repo directly (Require C++ compiler):
pip install git+https://github.com/rathaROG/lapx.git
Or clone and build on your local machine (Require C++ compiler):
git clone https://github.com/rathaROG/lapx.git
cd lapx
python -m pip install --upgrade pip
pip install "setuptools>=67.2.0"
pip install wheel build
python -m build --wheel
cd dist
lapx
is just the name for package distribution.
The same as lap
, use import lap
to import; for example:
import lap
import numpy as np
print(lap.lapjv(np.random.rand(4, 5), extend_cost=True))
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
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)]