nanolsap 0.1.4
Python module to solve the linear sum assignment problem (LSAP) low memory friendly
License: BSD-3-Clause
Requires Python: >=3.9
Keywords: lsap, munkres, kuhn-munkres, linear sum assignment problem

# nanolsap

A Python module to solve the linear sum assignment problem (LSAP) efficiently.
Extracted from SciPy, and modified to minimal memory cost.

## Description

```
from nanolsap import linear_sum_assignment
```
```
Solve the linear sum assignment problem.

Parameters
----------
cost_matrix : array
    The cost matrix of the bipartite graph.
    It should be 2-D ArrayLike object, with nr rows and nc cols.

maximize : bool (default: False)
    Calculates a maximum weight matching if true.

subrows : array (default: None)
    Use sub rows from cost matrix if not None.

subcols : array (default: None)
    Use sub cols from cost matrix if not None.

Returns
-------
row_ind, col_ind : array
    An array of row indices and one of corresponding column indices giving
    the optimal assignment. The cost of the assignment can be computed
    as ``cost_matrix[row_ind, col_ind].sum()``. The row indices will be
    sorted if subrows and subcols are both None; in the case of a square cost
    matrix they will be equal to ``numpy.arange(cost_matrix.shape[0])``.
```

This module is useful in cases when you need an efficient LSAP solver on 
very large cost_matrix and limited memory. 

For a cost_matrix with dtype float64 has shape of 30000\*30000, it needs  30000\*30000\*8 / 1024\*\*3 = 6.7GB memory to store it. 
In scipy.optimite.linear_sum_assignment, it will first convert it to float64 contiguous numpy 2-D array, then do a copy, and finally starts the solver. 
So, the actual memory cost is at least 6.7\*2 = 13.4GB. 
if the origin cost_matrix does not match, for example a float32 2-D array, 
the  first step here will cause one extra copy, increases the actual memory cost to 6.7/2+6.7\*2 = 16.75GB. 

In this module, When input cost_matrix is a contiguous numpy 2-D array, the solver can run on it directly without any copy. 
Also, cost_matrix can use small dtype like float32 to half reduce memory, so 3.35GB memory is enough. 

Notice: when nr > nc, scipy.optimize.linear_sum_assignment will copy then transpose and rearrange cost matrix so keeps memory access locality,
but this module do not do this, so it is about 2x slower in this situation. 
For nr <= nc, this module has almost no performance drop, 
so you can manually construct a transposed cost matrix for this module, 
and manually swap row_ind and col_ind result if nr > nc to get better performance. 

The subrows and subcols arguments allow solver run on only a subgroup of row and cols on cost_matrix. 
The result should be same as scipy.optimize.linear_sum_assignment(cost_matrix[np.ix_(subrows, subcols)]), but it avoids the expensive construct of sub cost_matrix.

## License

The code in this repository is licensed under the 3-clause BSD license, except
for files including a different license header.

The LSAP solver copied from [SciPy](https://github.com/scipy/scipy ) is also licensed under the 3-clause BSD
license.

Some files copied from [minilsap](https://github.com/ntamas/lsap ) are  also licensed under the 3-clause BSD license.