Description: 

This short course gives an introduction into numerical methods for PDEconstrained optimization. It covers numerical solution of PDEs and sensitivity computations and also briefly reviews some preliminaries from numerical linear algebra and numerical optimization. Finally, some more advanced topics are briefly outlined.
The 3 hour course was presented to a group of PhD students in the Doktorandenkolleg in Weissensee, Austria. The material consists of lecture slides and Julia code.

Acknowledgement: 

Thanks to MarieTherese Wolfram for covering my travel expenses and to the Doktorandenkolleg for providing additional funds.

Times: 

September 59, 2016

Slides: 

The slides can be downloaded here

Codes: 

Use the following Julia notebooks to reproduce the examples on the slides.
Some codes require the installation of our Julia package for PDEconstrained optimization jInv.
You can run these notebooks, for example, using JuliaBox. JuliaBox is also a great way to get started in Julia without installing it on your computer.
 exConvergenceGrad.ipynb  testing convergence of discrete derivative.
 checkDerivative.ipynb  example for testing derivatives.
 BacktrackedLinesearch.ipynb  illustration of backtracking and Armijo's condition.
 exLaplaceWithSD.ipynb  solving Laplace equation with Steepest Descent.
 exPCGforPoisson.ipynb  comparison of CG preconditioners.
 exDCResistivity.ipynb  case study of a small DC resistivity survey.
