gob 0.0.6

A collection of global optimization algorithms implemented in C++ and linked with Python.

Global Optimization Benchmark (GOB)

GOB is a collection of global optimization algorithms implemented in C++ and linked with Python. It also includes a set of analytical benchmark functions and a random function generator (PyGKLS) to test the performance of these algorithms.

Algorithms

• AdaLIPO+
• AdaRankOpt
• Bayesian Optimization
• CMA-ES
• Controlled Random Search
• DIRECT
• Every Call is Precious
• Multi-Level Single-Linkage
• Social Only Particle Swarm Optimization
• Langevin dynamics
• Stein Boltzmann Sampling
• Consensus Based Optimization
• Common noise variants of McKean-Vlasov dynamics
• Gradient Descent
• Multi-start Gradient Descent
• Pure Random Search

Documentation

The documentation is available at gaetanserre.fr/GOB.

Installation (Python ≥ 3.10)

Install the package via pip from PyPI:

pip install gob

Alternatively, download the corresponding wheel file from the releases and install it with pip:

pip install gob-<version>-<architecture>.whl

Build from source

Make sure you have CMake (≥ 3.28), a c++ compiler, and the eigen3 library installed. Then clone the repository and run:

pip install . -v

It should build the C++ extensions and install the package. You can also build the documentation with:

cd docs
pip install -r requirements.txt
make html

Usage

This package can be used to design a complete benchmarking framework for global optimization algorithms, testing multiple algorithms on a set of benchmark functions. See test_gob.py for an example of how to use it.

The global optimization algorithms can also be used independently. For example, to run the AdaLIPO+ algorithm on a benchmark function:

from gob.optimizers import AdaLIPO_P
from gob import create_bounds

f = lambda x: return x.T @ x

opt = AdaLIPO_P(create_bounds(2, -5, 5), 300)
res = opt.minimize(f)
print(f"Optimal point: {res[0]}, Optimal value: {res[1]}")

See test_optimizers.py for more examples of how to use the algorithms.

Contributing

Contributions are welcome! Please see the CONTRIBUTING file for guidelines.

References

• BayesianOptimization
• libcames
• nlopt-python

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). See the LICENSE file for details.