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https://github.com/bbopt/stomads

StoMADS algorithm for stochastic blackbox optimization

https://github.com/bbopt/stomads

Science Score: 23.0%

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    Found 13 DOI reference(s) in README
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Keywords

blackbox-optimization complexity-analysis convergence-analysis derivative-free-optimization direct-search matlab optimization probability statistics stochastic-optimization stochastic-processes
Last synced: 5 months ago · JSON representation

Repository

StoMADS algorithm for stochastic blackbox optimization

Basic Info
  • Host: GitHub
  • Owner: bbopt
  • License: gpl-3.0
  • Language: MATLAB
  • Default Branch: main
  • Homepage:
  • Size: 85 KB
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Topics
blackbox-optimization complexity-analysis convergence-analysis derivative-free-optimization direct-search matlab optimization probability statistics stochastic-optimization stochastic-processes
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.md

StoMADS algorithm for stochastic blackbox optimization

StoMADS is an extension of the MADS algorithm, and is developed for the optimization of stochastically noisy objective functions, i.e., whose values can be computed through a blackbox corrupted by some random noise. It is a direct-search algorithm which takes as input a vector of decision variables.

Prerequisites

  • StoMADS is implemented using Matlab R2021a.
  • To use the 40 problems from the YATSOp repository, email poptus@mcs.anl.gov (Mathematics and Computer Science division of Argonne National Laboratory, USA) or see link if it is available (more details below).

Installation of StoMADS

No installation is required. Users simply need to download the stomads folder, and use either stomadsapplications.m or stomadsexperiments.m to run the algorithm (more details below).

Getting started

In the stomads folder, users have two options, using either stomadsapplications.m or stomadsexperiments.m to run StoMADS.

  • stomads_applications.m runs StoMADS on unconstrained problems, or problems with bound constraints. It aims to show users how to provide problems to the algorithm. Other very detailed information is provided in the file.
  • stomadsexperiments.m runs StoMADS in an automated way on the 40 problems (from the YATSOp repository) considered in the numerical section of the STARS paper, for various types of noise (additive, multiplicative, Gaussian, uniform, etc.), and various noise levels via their standard deviations. It generates solutions files, stats files and history files in a 'StoMADSOutput' folder, which can be used to generate data profiles, performance profiles, trajectory plots, convergence graphs, etc. Users can therefore refer to the numerical section of the STARS paper for more details on the use of this script. Other very detailed information is provided in the stomads_experiments.m file.

Regarding the YATSOp repository

  • See above about how to access the repository.
  • There is another YATSOp folder inside the StoMADSMainFiles subfolder of stomads, which contains a single Readme.txt file. Information is provided in this txt file on how to add the location of the YATSOp folder to the Matlab path.

Citing StoMADS

If you use StoMADS, please cite the following paper.

```

@article{AuDzKoDi2021, Author = {C. Audet and K.J. Dzahini and M. Kokkolaras and S. {Le~Digabel}}, Title = {Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates}, Journal = {Computational Optimization and Applications}, Year = {2021}, Volume = {79}, Number = {1}, Pages = {1--34}, Doi = {10.1007/s10589-020-00249-0}, Url = {https://doi.org/10.1007/s10589-020-00249-0} }

``` DOI

Owner

  • Name: Blackbox Optimization
  • Login: bbopt
  • Kind: organization
  • Email: nomad@gerad.ca
  • Location: Montréal, Qc, Canada

GitHub organization of the research group from Polytechnique Montréal and GERAD for derivative-free and blackbox optimization

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