TheMonocledHamster / neural-knapsack

Code related to the paper arXiv:2005.14105.

Neural Knapsack

This repository contains the source code related to the publication

Hertrich, Christoph and Skutella, Martin: Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size. arXiv:2005.14105.

Requirements

(with the versions used in our paper)

• python (3.8.5)
• numpy (1.19.1)
• tensorflow (2.2.0)
• statsmodels (0.11.1)
• matplotlib (3.3.1)

Note that numpy and tensorflow only guarantee the same outcome of random experiments with explicit seeds if exactly these versions are used.

Reproducing the experiments of our paper

Section 5 and Appendix A

For reproducing the experiment with threshold 0.005 one only needs to run 'knapsack_experiments.py' and 'knapsack_analyze.py' in this order. The latter will output a plot as included in our paper, as well as, a summary of the least squares regression including the reported p-value.

For reproducing the experiment with threshold 0.0025, please modify the following parameters in the header of 'knapsack_experiments.py':

results_dir = './results/t0.0025_seed257/'
pstars = range(1,26,1)
break_at_threshold = 0.0025

Please also modify the following parameters of 'knapsack_analyze.py':

threshold = 0.0025
filename = './results/t0.0025_seed257/results.json'

Then proceed as above.

For reproducing the experiment with threshold 0.00375, please modify the following parameters in the header of 'knapsack_experiments.py':

results_dir = './results/t0.00375_seed257/'
pstars = range(2,51,2)
break_at_threshold = 0.00375

Please also modify the following parameters of 'knapsack_analyze.py':

threshold = 0.00375
filename = './results/t0.00375_seed257/results.json'

Then proceed as above.

If you have any questions, please do not hesitate to contact us.