4 releases (stable)
Uses old Rust 2015
1.1.1 | Jun 15, 2020 |
---|---|
1.1.0 | Mar 16, 2018 |
1.0.0 | Mar 15, 2018 |
0.1.0 | Mar 14, 2018 |
#951 in Algorithms
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hungarian
IMPORTANT: The pathfinding
crate has a significantly
faster implementation of this
algorithm (benchmarks below), uses traits to abstract over cost matrices, and is also better maintained.
I recommend using it instead.
A simple Rust implementation of the Hungarian (or Kuhn–Munkres) algorithm.
Should run in O(n^3)
time and take O(m*n)
space, given an m * n
rectangular
matrix (represented as a 1D slice).
Derived and modified from this great explanation.
Usage
Add the following to your Cargo.toml
file:
[dependencies]
hungarian = "1.1.1"
Add the following to the top of your binary or library:
extern crate hungarian;
use hungarian::minimize;
And you should be good to go! For more information, check out the documentation.
Recent Changes
- 1.1.1
- Version bump so the
pathfinding
redirect appears oncrates.io
.
- Version bump so the
- 1.1.0
- Greatly optimized performance (by a factor of 2-4 on benchmarks on matrices from 5x5 to 100x100)
- Now uses
num-trait
to take generic integer weights - Now backed by
ndarray
to scale better with larger inputs
- 1.0.0
- Greatly improved source code documentation
- Renamed
hungarian
function tominimize
- Now handle arbitrary rectangular matrices
- Added more test cases to cover non-square matrices
- Now returns
Vec<Option<Usize>>
to handle when not all columns are assigned to rows
- 0.1.0
- Initial release
- Working base algorithm, but only works for square matrices.
- Not well documented
Notes
Instead of using splitting logic across files and helper functions, I tried to simplify and condense the above explanation into a single, simple function while maintaining correctness. After trawling the web for test cases, I'm reasonably confident that my implementation works, even though the end result looks fairly different.
Please let me know if you find any bugs!
Performance
Benchmarks were obtained using Criterion.rs, with the following two types of cost matrices:
Worst Case | Generic Case
|
------------- | -------------
| 1 | 2 | 3 | ... | | 1 | 2 | 3 |
------------- | -------------
| 2 | 4 | 6 | ... | | 4 | 5 | 6 |
------------- | -------------
| 3 | 6 | 9 | ... | | 7 | 8 | 9 |
------------- | -------------
. . . |
. . . |
. . . |
|
C(i, j) = (i + 1)(j + 1) | C(i, j) = (i * width) + j
Criterion Results
Cost Matrix | Matrix Size | hungarian Average Runtime |
pathfinding Average Runtime |
---|---|---|---|
Worst-Case | 5 x 5 | 2.42 us | 1.19 us |
Worst-Case | 10 x 10 | 20.38 us | 4.24 us |
Worst-Case | 25 x 25 | 546.88 us | 59.66 us |
Worst-Case | 50 x 50 | 6.97 ms | 422.05 us |
Generic | 5 x 5 | 1.75 us | 871.24 ns |
Generic | 10 x 10 | 7.49 us | 3.50 us |
Generic | 25 x 25 | 86.33 us | 33.91 us |
Generic | 50 x 50 | 556.48 us | 285.69 us |
Generic | 100 x 100 | 3.97 ms | 1.93 ms |
Measured on a quad-core 2.6GHz Intel(R) i7-6700HQ with 16GB RAM; using Ubuntu 16.04 Linux x86_64 4.8.0-53-generic
Dependencies
~1.5MB
~28K SLoC