Null space algorithm for nonlinear constrained optimization
Project description
Null space optimizer
nullspace_optimizer
is a package implementing the null space algorithm for nonlinear constrained
optimization.
Please cite the following references when using this source:
FEPPON, Florian, ALLAIRE, Grégoire, et DAPOGNY, Charles. Null space gradient flows for constrained optimization with applications to shape optimization. 2019. HAL preprint hal-01972915.
FEPPON, Florian. Shape and topology optimization of multiphysics systems. 2019. Université Paris-Saclay. Thèse préparée à l'École polytechnique.
Installation
# Light version
pip install nullspace_optimizer
# Full dependencies including colored output and plotting features
pip install nullspace_optimizer[colored,matplotlib]
Running examples
A few examples of 2-d inequality constrained optimization are available in the `examples' folder. They can be run from command line with
python -m nullspace_optimizer.examples.ex0
python -m nullspace_optimizer.examples.ex1
python -m nullspace_optimizer.examples.ex2
and so on.
For instance, running python -m nullspace_optimizer.examples.ex1
should produce the following figure:
Requirements
Runs with python 3.6 and the following libraries:
- numpy (>=1.12.1)
- scipy (>=0.19.1)
- cvxopt (>=1.2.1)
Optional dependencies:
- colored (>=1.3.93) (for colored output)
- matplotlib (>=2.0.2) (for displaying figures while running examples)
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