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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: Null space gradient flow trajectories

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)

Project details


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