AeroLab Optimization Software
Created by:
NOTE FOR LEGACY OPTIX USERS:
Since January of 2019, API of Optix has changed significantly to somewhat mirror that of scipy.optimize. We continue working to improve the API and functionality of Optix. This README will always contain up to date information for using Optix.
Optix is a gradient-based optimization tool designed with aerodynamics in mind. Recognizing that aerodynamic functions are often computationally intensive, Optix has been designed to be as light-weight and parallel as possible, while offering an intuitive API.
The algorithms have been developed, for the most part, using the following references:
THIS SOFTWARE COMES WITH ABSOLUTELY NO WARRANTY EXPRESSED OR IMPLIED
All functionality of Optix is wrapped within the minimize(). The following is an example of how this function is used:
import optix as opt
from random import random
def f(x):
return x[0]**4-2*x[1]*x[0]**2+x[1]**2+x[0]**2-2*x[0]+5
x0 = [-10+20*random(),-10+20*random()]
optimum = opt.minimize(f,x0,file_tag="_test",n_search=8,max_processes=8,line_search="quadratic",termination_tol=1e-6,verbose=False,hess_init=1)
print("Optimum value: {0}".format(optimum.f))
print("Optimum point: {0}".format(optimum.x))
print("Function calls: {0}".format(optimum.obj_calls))
Running optix.minimize()
will also output results to 3 text files. These 3 files are named iterations
, gradient
, and evaluations
, each appended with the user-specified file tag. The first two are written to during runtime. The last is written to only after successful completion of the optimization. Optimize mimics what is printed to the command line, giving information about the fitness, point in the design space, magnitude of steps, etc. Gradient gives information about the objective and constraint gradients at each point in the optimization. Evaluations simply outputs the value of the objective function at each point considered during optimization, including points used to calculate finite differences.
For documentation, please refer to the docstrings. This can be done either by using the built in python help() command in the interpreter or by refering to the source. For example, to view the minimize()
parameters and return values, type:
help(optix.minimize)
into a Python interpreter. Please note that Optix should be imported first.
The source code can be found at https://github.com/usuaero/Optix
You can either download the source as a ZIP file and extract the contents, or
clone the Optix repository using Git. If your system does not already have a
version of Git installed, you will not be able to use this second option unless
you first download and install Git. If you are unsure, you can check by typing
git --version
into a command prompt.
Master
Clone or download
buttonDownload ZIP
git clone https://github.com/usuaero/Optix
The Optix package can be installed by navigating to the root directory of the project and using the following command
pip install .
This installs Optix as a package on your machine, meaning you can import Optix into any Python script without having to copy around the source files
Unit tests are are run using the following command:
py.test
Please note that you must have Pytest installed for this to work.
For bugs, please create an issue on the github repository.
This project is licensed under the MIT license. See LICENSE file for more information.