(also known as Hungarian algorithm or Kuhn algorithm)

It is designed to be used first from other lanuguages, particularly ones where the implementation of this approach is much slower, notably, from python.

import numpy as np
import system

cost_matrix = np.random.rand(100, 100)
np.savetxt(open('input.txt', 'w'), cost_matrix)
os.system('bin/hungarian_solver %d %d < input.txt > output.txt' % cost_matrix.shape)
assignment = np.int32(np.loadtxt(open('output.txt', 'r')))
solution_cost = np.sum(cost_matrix[assignment[:, 0], assignment[:, 1]])

bin/hungarian_solver matrix_row_num matrix_col_num [< input_file [> output_file]]

make

You can also call 'bin/test' to see the speed of the approach. For example, for 300x300 matrices, it computes the result in ~1s., while the python 'munkres' requires 176s.

If you need matrices bigger than 6kx6k, or values bigger than 1e6 in magnitude, modify the appropriate constants in src/hungarian_assignment.h.

####NOTE Traditionally, minimum cost matching is performed. This approach computes maximum cost mapping. Multiply your matrix by -1 if you need the former.