https://github.com/gaetanserre/pygkls
pyGKLS is a Python wrapper for the GKLS generator of global optimization test functions (Giavano et al., 2003).
https://github.com/gaetanserre/pygkls
generator objective-function objective-function-optimization objective-functions optimization
Last synced: 6 months ago
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pyGKLS is a Python wrapper for the GKLS generator of global optimization test functions (Giavano et al., 2003).
- Host: GitHub
- URL: https://github.com/gaetanserre/pygkls
- Owner: gaetanserre
- License: gpl-3.0
- Created: 2024-07-11T15:00:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-12-11T10:29:31.000Z (6 months ago)
- Last Synced: 2025-12-12T10:55:06.606Z (6 months ago)
- Topics: generator, objective-function, objective-function-optimization, objective-functions, optimization
- Language: C++
- Homepage:
- Size: 588 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## pyGKLS
[](https://github.com/gaetanserre/pyGKLS/actions/workflows/build.yml)
[](https://badge.fury.io/py/gkls)
pyGKLS is a Python wrapper for the GKLS generator of global optimization test functions ([Giavano et al., 2003](https://dl.acm.org/doi/10.1145/962437.962444)). It uses the original C implementation of the generator and provides a Python interface using Cython to generate the test functions. pyGKLS encompass a C++ class that wraps the original C implementation to provide a more user-friendly interface that can be used in C++ projects (see `src/example.cc`) or Python projects (see `test.py`).
### Random number generator
The original GKLS generator uses a random number generator introduced by Knuth in his book "The Art of Computer Programming". pyGKLS uses the Mersenne Twister random number generator from the C++ standard library to generate random numbers.
### Installation
To install pyGKLS, one needs to have `Python 3.12` or later.
Run the following command:
```bash
pip install gkls
```
### Usage
The Python interface is simple and easy to use. Here is an example of how to generate a GKLS function:
```python
from gkls import GKLS
# Create an instance of the GKLS class with random generation (default)
gkls = GKLS(2, 2, [-1, 1], -1)
x = [0.5, 0.5]
print(f"D_f = {gkls.get_d_f(x)}")
print(f"D2_f = {gkls.get_d2_f(x)}")
print(f"ND_f = {gkls.get_nd_f(x)}")
print(f"D_grad = {gkls.get_d_grad(x)}")
print(f"D2_grad = {gkls.get_d2_grad(x)}")
print(f"D2_hessian = {gkls.get_d2_hess(x)}")
```
One output of the above code (stochastic) could be:
```
D_f = 2.0314828290164897
D2_f = 2.0314828290164897
ND_f = 2.0314828290164897
D_grad = [1.7408628759925895, 2.2572832704507357]
D2_grad = [1.7408628759925895, 2.2572832704507357]
D2_hessian = [[2.0, 0.0], [0.0, 2.0]]
```
Arguments can be passed to the `GKLS` constructor function to control the properties of the generated function. The constructor has the following signature:
```python
GKLS(
dim : int, # dimension of the function
num_minima : int, # number of local minima
domain : List[float], # domain of the function (i.e. [domain_low, domain_high])
global_min : float # global minimum value
global_dist=None : float, # distance from the paraboloid minimizer to the global minimizer
global_radius=None : float, # radius of the global minimizer attraction region
gen=None : None | "geometry" | int, # generator type. None for random, "geometry" for geometry-based, or an integer for a specific seed
)
```
See [`test.py`](test.py) for more examples of how to use the GKLS class.