Welcome to OMADS!

This is the documentation page of the OMADS python package.

About OMADS

OMADS is a python implementation of the mesh adaptive direct search (MADS) algorithm that supports orthogonal \(2n\) directions where \(n\) is the number of variables. MADS is a direct search method that has global convergence properties [1]. The latest version of OMADS provides three modules; POLL.py, SEARCH.py and MADS.py. Each module can work per se to solve an optimization problem given an appropriate problem setup. OMADS is published on PyPi and maintained on my GitHub page [2].

Citation

If you use this code, please cite it as below.

@software{OMADS_AB,
author       = {Bayoumy, A.},
title        = {OMADS},
year         = 2022,
publisher    = {Github},
version      = {2.1.0},
url          = {https://github.com/Ahmed-Bayoumy/OMADS}
}

References

1

C. Audet and J. Dennis. Mesh Adaptive Direct Search Algorithms for Constrained Optimization. SIAM Journal on optimization, 17(1):188–217, 2006.

2

A. Bayoumy. OMADS: Orthogonal Mesh Adaptive Direct Search. 2022. URL: https://github.com/Ahmed-Bayoumy/OMADS, doi:10.5281/zenodo.7212701.

3

C. Audet, S. Digabel, V. Rochon Montplaisir, and C. Tribes. Algorithm 1027: NOMAD Version 4: Nonlinear Optimization with the MADS Algorithm. ACM Transactions on Mathematical Software, 48(3):35:1–35:22, 2022. URL: https://dx.doi.org/10.1145/3544489, doi:10.1145/3544489.

4

A. Bayoumy. StatLML: Statistical Machine Learning Library. 2022. URL: https://github.com/Ahmed-Bayoumy/SLML.

5

C. Audet and W. Hare. Derivative-free and Blackbox Optimization. Volume 2. Springer, 2017.

6

A. Bayoumy. RAF: Relative Adequacy Framework. 2022. URL: https://github.com/Ahmed-Bayoumy/RAF.

Technical Documentation

Demo Documentation

Code Documentation