dolfinx_optim documentation

`dolfinx_optim` documentation#

Introduction#

dolfinx_optim is a convex optimization add-on package to the FEniCSx finite-element library. It provides a simple Domain-Specific Language through FEniCSx dolfinx Python interface for solving convex optimization problems. In particular, it relies on the Mosek mathematical programming library. Mosek provides a state-of-the art interior-point solver for linear programming (LP), convex quadratic programming (QP), second-order conic programming (SOCP) and semi-definite programming (SDP).

Github repository: bleyerj/dolfinx_optim
Online documentation: https://bleyerj.github.io/dolfinx_optim/

Prerequisites#

dolfinx_optim requires:

FEniCSx (v.0.8), see installation instructions here.
MOSEK (>= version 10 with its Python Fusion interface), see installation instructions here. The Python interface can be simply installed via pip:

pip install -f https://download.mosek.com/stable/wheel/index.html Mosek

Mosek is a commercial software so users need a valid Mosek license. Free unlimited licenses are available for education and research purposes, see the Academic License section.

Installation and usage#

Simply clone the dolfinx_optim public repository

https://github.com/bleyerj/dolfinx_optim

and install the package by typing

pip install dolfinx_optim/ --user

Features#

Current version supports variational problem formulations resulting in Linear Programs, Second-Order Cone Programs, Semi-Definite Programs and Power or Exponential cone programs.

License#

All this work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License license .

Citing#

Papers related to this project can be cited as:

Bleyer, Jeremy. “Automating the formulation and resolution of convex variational problems: applications from image processing to computational mechanics.” ACM Transactions on Mathematical Software (TOMS) 46.3 (2020): 1-33.

@article{bleyer2020automating,
  title={Automating the formulation and resolution of convex variational problems: applications from image processing to computational mechanics},
  author={Bleyer, Jeremy},
  journal={ACM Transactions on Mathematical Software (TOMS)},
  volume={46},
  number={3},
  pages={1--33},
  year={2020},
  publisher={ACM New York, NY, USA}
}

Bleyer, Jeremy. “Applications of conic programming in non-smooth mechanics.” Journal of Optimization Theory and Applications (2022): 1-33.

@article{bleyer2022applications,
  title={Applications of conic programming in non-smooth mechanics},
  author={Bleyer, Jeremy},
  journal={Journal of Optimization Theory and Applications},
  pages={1--33},
  year={2022},
  publisher={Springer}
}