GP BVP solver

This is the companion code for the paper GOODE: A Gaussian Off-the-shelf Ordinary Differential Equation Solver by David John, Michael Schober and Vincent Heuvline. The paper is submitted to ICML 2019 and can be found here. The code allows the users to reproduce and extend the results reported in the study. Please cite the above paper when reporting, reproducing or extending the results.

@InProceedings{pmlr-v97-john19a,
  title = 	 {{GOODE}: A {G}aussian Off-The-Shelf Ordinary Differential Equation Solver},
  author = 	 {John, David and Heuveline, Vincent and Schober, Michael},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {3152--3162},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Long Beach, California, USA},
  month = 	 {09--15 Jun},
  publisher = 	 {PMLR}
}

Purpose of the project

This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.

Requirements, how to build, test, install, use, etc.

The code was developed and works with Matlab R2018a.

Download the bvp Testset from https://archimede.dm.uniba.it/~bvpsolvers/testsetbvpsolvers/?page_id=27 and copy to the folder external/bvpTestSet. Download the code TOM from https://archimede.dm.uniba.it/~mazzia/mazzia/?page_id=433 and copy to the folder external/tom.

Run the setup.m file to add required folders to the path. The code for the GB BVP solver GOODE is in the folder GPs. The folder experiments contains scripts used to produce the results in the paper.

License

GP BVP solver is open-sourced under the MIT license. See the LICENSE file for details.

For a list of other open source components included in GP BVP, see the file 3rd-party-licenses.txt.