Skip to main content

Neighborhood Algorithm Optimization and Ensemble Appraisal

Project description

Travis CI: image0

Python 3 implementation of “neighborhood algorithm” direct-search optimization and Bayesian ensemble appraisal. In short, a nearest-neighbor interpolant based on Voronoi polygons is used to interpolate the misfit (search) and posterior probability (appraisal) to allow efficient sampling and integration for high-dimensional problems. Details on theory and implementation are supplied in the references.

Example search population for 4D Rosenbrock objective function. Image include 10,000 samples collected in 1,000 iterations of the neighborhood algorithm direct search, with num_samp=10 and num_resamp=5. The true minimum is 0 at (1, 1, 1, 1), while the best sample is 0.0113 at ((0.976, 0.953, 0.908, 0.824). This result continues to converge for larger sample size (but the plot is less interesting since the density converges to a point!)

To generate the example figure above, you can run the internal demo, like so:

import neighborhood as nbr

nbr.demo_search(ndim=4, nsamp=10, nresamp=5, niter=500)

Equivalently, you can do the following:

import neighborhood as nbr

num_dim = 4
srch = nbr.Searcher(
    limits=[(-1.5, 1.5) for _ in range(num_dim)],


Optimization is implemented, ensemble appraisal is in progress.


This project uses pytest for unit testing. The aim is not to be exhuastive, but to provide reasonable assurances that everything works as advertised. To run, simply call pytest --verbose from somewhere in this package.


Release versions are tagged in the repository, built as distributions, and uploaded to PyPI. The minimal commands to do this are:

# update PyPI-readable README
pandoc --from=markdown --to=rst --output=README.rst
# build with setuptools
python3 sdist bdist_wheel
# upload to PyPI test server (then check it out)
twine upload --repository-url dist/*
# upload to PyPI
twine upload dist/*
# tag release in git repo
git tag -a X.X.X -m "vX.X.X"
git push origin --tags


  1. Sambridge, M. (1999). Geophysical inversion with a neighbourhood algorithm - I. Searching a parameter space. Geophysical Journal International, 138(2), 479–494.

  2. Sambridge, M. (1999). Geophysical inversion with a neighborhood algorithm -

  1. Appraising the ensemble. Geophys, J. Int., 138, 727–746.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neighborhood-0.1.1.tar.gz (8.1 kB view hashes)

Uploaded source

Built Distribution

neighborhood-0.1.1-py3-none-any.whl (11.1 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page