Convex Optimization

Content

The lecture gives an introduction into the field of convex optimization and details the most important numerical methods for the solution of convex optimization problems.

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Lecture Notes

Literature

https://us02web.zoom.us/j/87013567712?pwd=LzNBejNLR01KaHlZUDl4eHVIRjlRdz09

Meeting ID: 870 1356 7712 Passcode: 488223

Exercises

You are supposed to solve at least 50% of the numerical exercises (Matlab, Pyhton) in order to participate in the exam. There will be no points, only “OK”, “Not OK” or “ ” if nothing was handed in

Introduction to Matlab and CVX

CVX and Python

Solutions to Practical Exercises

Software Practical: Convex Optimization

The software practical is suited for students attending the lecture on Convex Optimization that additionally wish to apply the algorithms and concepts to concrete examples in order to get a deeper understanding.

Registration: in the lecture, or mail to Stefania Petra.

Assignment

First choose a paper and a concrete problem instance like denoising, deblurring, segmentation, reconstruction (Radon, MRI), optimal transport. Your task is to implement the algorithm in MATLAB or Python. Write a small report with 5-8 pages to describe the algorithm, convergence properties and your problem setup along with the results of your experiments. Please use the latex template files.

You can hand in your code+report at the end of the this term.

Imaging Problems

Paper

Note, if a paper list of papers is already taken, you can ask for a similar one.