/Galerkin-Conditional-Gradient

This module solves a PDE constrained minimisation problem with TV-regularization, using the method described in the paper "Conditional gradient for total variation regularization with PDE constraints: a graph cuts approach"

Primary LanguagePython

Galerkin-Conditional-Gradient

DOI

Authors: Giacomo Cristinelli, José A. Iglesias, Daniel Walter

This module solves the control problem with Total Variation regularization

$$\min_{u\in BV(\Omega)} \frac{1}{2} |Ku-y_o|^2 + \alpha TV(u,\Omega)$$

where K is the control-to-state operator associated with a Poisson-type PDE.

It employs the method described in the paper "Conditional gradients for total variation regularization with PDE constraints: a graph cuts approach". Preprint available at: https://arxiv.org/abs/2310.19777

Important libraries:

FEniCS (Dolfin) --version 2019.1.0 (https://fenicsproject.org/)

Maxflow (http://pmneila.github.io/PyMaxflow/maxflow.html)