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A modification of Newton's method for nondifferentiable equations

Build Status codecov

This is a Reproduction Package as described in the manuscript "Three Empirical Principles for Computational Reproducibility and their Implementation: The Reproduction Package" by M. S. Krafczyk, A. Shi, A. Bhaskar, D. Marinov, & V. Stodden.

This Reproduction Package does not contain the run_all.sh script because all computations run extremely quickly, and there is no need to run a reduced set of experiments.

"A modification of Newton's method for nondifferentiable equations" is an implementation of the techniques presented in A modification of Newton's method for nondifferentiable equations

Build Instructions

Requirements

Instructions were tested using Docker version 18.06.0-ce, build 0ffa825, on Ubuntu 16.04.5 LTS.

Building with Docker

docker build -t ${DOCKER_IMAGE_NAME} .

Run Instructions

Running with Docker

To start a container for the Docker image:

docker run -it --rm -v $(pwd):/Scratch ${DOCKER_IMAGE_NAME}

Run Everything

Within the Docker container, to run everything, computational scripts for experiments and visualization scripts, run

./run.sh

Please be aware of computational efforts for the scripts. More details can be found here.

See sections below provide for details about the individual steps.

Running Computational Scripts

Within the Docker container, run

./computation.sh

Output will be computed_results.txt.

Expected results is `expected_results.txt'.

The script also runs

python check_results.py expected_results.txt computed_results.txt

to automatically check the computed_results.txt against expected_results.txt

Reproduction Notes

We kept track of our progress and issues inside notes.txt. We also have an jupyter notebook showing this progress over time ReproducibilityPlot.ipynb.

Acknowledgements

We want acknowledge the authors for their fine work on this experiment. We succeeded with this project where many others had failed. The authors should be commended on putting together high quality work.

Grant Acknowledgement

This work was partially funded by NSF grants OAC-1839010 and CNS-1646305.

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