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 is a lower-level CUDA implementation of the same algorithm. Note: numba_auction is a numba implementation of the same algorithm.
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: 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.
- Would this give any speedups?
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