https://github.com/bkj/auction-lap

Auction algorithm for solving linear assignment problem (LAP)
https://github.com/bkj/auction-lap

auction-algorithm auction-lap linear-assignment-problem pytorch

Last synced: 8 months ago
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Auction algorithm for solving linear assignment problem (LAP)

• Host: GitHub
• URL: https://github.com/bkj/auction-lap
• Owner: bkj
• Created: 2018-06-21T02:27:00.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T21:18:47.000Z (over 3 years ago)
• Last Synced: 2023-03-02T09:51:59.203Z (almost 3 years ago)
• Topics: auction-algorithm, auction-lap, linear-assignment-problem, pytorch
• Language: Python
• Homepage:
• Size: 104 KB
• Stars: 61
• Watchers: 2
• Forks: 15
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

          
#### auction-lap

Linear Assignment Problem (LAP) solver using the auction algorithm.  Implemented in `pytorch`, runs on CPU or GPU.

Runtime scales w/

 - the dimension of the matrix

 - the range of entries in the matrix (max value - min value)

__Note:__ [cuda_auction](https://github.com/bkj/cuda_auction) is a lower-level CUDA implementation of the same algorithm.

__Note:__ [numba_auction](https://github.com/bkj/numba_auction) is a numba implementation of the same algorithm.

##### Installation

```

conda create -n auction_env python=3.6 pip

source activate auction_env

pip install -r requirements.txt

conda install pytorch==0.3.1 torchvision cuda91 -c pytorch -y

```

##### Usage

```

usage: benchmark.py [-h] [--max-entry MAX_ENTRY] [--min-dim MIN_DIM]

                    [--max-dim MAX_DIM] [--n-evals N_EVALS] [--eps EPS]

                    [--seed SEED]

optional arguments:

  -h, --help            show this help message and exit

  --max-entry MAX_ENTRY

                        maximum entry in matrix

  --min-dim MIN_DIM     minimum dimension matrix to test

  --max-dim MAX_DIM     maximum dimension matrix to test

  --n-evals N_EVALS     number of steps between min and max matrix size

  --eps EPS             "bid size" -- smaller values give better accuracy w/

                        longer runtime

  --seed SEED           random seed

```

See `./run.sh` for examples.

##### Results

![100-exact-time](imgs/time-100-exact.png)

![1000-approx-time](imgs/time-1000.png)

![1000-approx-score](imgs/score-1000.png)

##### To Do

 - Would this give any speedups?

    - https://github.com/rusty1s/pytorch_scatter