I have a set $S = \{s_1, \cdots, s_n \}$ ($n$ is even), and the $n\times n$ matrix $M = \{ d_{ij}\}$, where $d_{ij}$ is a distance between $s_i$ and $s_j$. I wish to find a non-bipartite matching on $S$ that minimises the sum of the distances between matched elements.

That is to say, I want to break the set $S$ into $n/2$ pairs $\{P_k = (s_k^1, s_k^2) \}$ so that each $s_i$ appears in exactly one pair, with exactly one other element $s_j$, $i \neq j$, and the pairs use up all of $S$. Further, the sum of distances within pairs is minimised.

I couldn't find a Python implementation of the problem, but I found some implementations of the assignment problem. I utilised these by finding bipartite matchings between $S$ and itself. To prevent trivial matchings $s_i \leftrightarrow s_i $, I set the diagonal of the matrix $M$ to a large number (say) $2 \times max \{d_{ij}\}$. For example, using

`scipy.optimize.linear_sum_assignment(M, maximize=False)`

or using $-M$ to solve the dual problem with:

`scipy.sparse.csgraph.maximum_bipartite_matching(-M, row)`

For small sets $S$, both of these give the correct desired non-bipartite matching. Each element $s_i$ matches with exactly one $s_j$ $(i \neq j)$, and the sum of distances is minimised. I have checked this by brute force. An example of correct matchings is:

`[[0, 6], [1, 8], [2, 15], [3, 4], [4, 3], [5, 7], [6, 0], [7, 5], [8, 1], [9, 11], [10, 13], [11, 9], [12, 17], [13, 10], [14, 16], [15, 2], [16, 14], [17, 12]]`

(With $n = 18$ and counting $[i,j]$ and $[j,i]$ as the same, we have $n/2$ matchings.)

But for large $n$, I start to see some errors. Things like $s_1$ matches with $s_{356}$ but $s_{356}$ matches with $s_8$ etc.

The vectors $s_i$ are randomly generated floats, so ties are extremely unlikely. Any thoughts on what could be going on?

To better explain the issue, I have posted some Python code on github:

https://github.com/zburq/scipy_linear_sum_assignment_issue/blob/main/polygon_issue.ipynb

## Best Answer

Your algorithm does this because it has no reason to avoid it; your code is not solving the problem you think it is solving.

When you use the $n\times n$ matrix $M$ to find a bipartite matching, you are solving a problem on a bipartite graph with $2n$ vertices; $n$ vertices for the rows and $n$

differentvertices for the columns. The algorithm is trying to match each row to a column.For small $n$, you may get lucky: you may be able to give each vertex its best option. If this happens, the result will look like it's giving you a matching of the original $n$-vertex graph. That's because if $M_{ij}$ is the smallest entry in row $i$, then $M_{ji}$ is the smallest entry in column $i$; so if both row $i$ and column $i$ get their best option, they are matched to column $j$ and row $j$, respectively.

But in general, there is simply no such constraint. It may be that the best matching pairs row $1$ with column $356$, but row $356$ is paired with column $8$.

Here is a simple example of a $6 \times 6$ matrix $M$ for which we can see this will happen. As you desire, I have set the diagonal of $M$ to a very high number.

$$ \begin{bmatrix} \infty & 0.1 & 0.2 & 10 & 20 & 30 \\ 0.1 & \infty & 0.3 & 40 & 50 & 60 \\ 0.2 & 0.3 & \infty & 70 & 80 & 90 \\ 10 & 40 & 70 & \infty & 0.4 & 0.5 \\ 20 & 50 & 80 & 0.4 & \infty & 0.6 \\ 30 & 60 & 90 & 0.5 & 0.6 & \infty \end{bmatrix} $$ Here, one possible best

bipartitematching is the matching $$ \{(1,2), (2,3), (3,1), (4,5), (5,6), (6,4)\} $$ with total weight $0.1+0.3+0.2+0.4+0.6+0.5 = 2.1$. This is not a legitimate matching of the $6$-vertex graph.In the $6$-vertex graph, we need to choose some edge that connects vertices $\{1,2,3\}$ to vertices $\{4,5,6\}$, all of which are much more expensive. The best matching uses edges $\{1,4\}$, $\{2,3\}$, and $\{5,6\}$ and has weight $10 + 0.3 + 0.6 = 10.9$.

You can reproduce this example with distances in the plane, if you take a set of $6$ points which form two clusters of $3$ points.