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The weekly exercises will appear here.
There will be an exam after the lecture part of the course.
In the project you are supposed to take a subset of the data with only few projections, such as 20 projection directions. Use one of these methods to recover the walnut slice from sparse data:
- Tikhonov regularization based on conjugate gradient method,
- approximate total variation regularization implemented iteratively with the Barzilai-Borwein method,
- some other suitable method.
Optimally, you should have an automatic method for choosing the regularization parameter and an automatic stopping criteria for the iteration. These are both difficult requirements, so have a simple approach as plan B if a more complicated approach does not work.
First goal consists of two things: (a) two first sections should be preliminary written in LaTeX (not necessarily in poster format yet) and (b) the Matlab codes at the following webpage should be run and studied:
Two things will be graded in the meeting about the first goal: (a) the draft of project work and (b) your understanding of the Matlab codes at the above webpage relevant to your topic. The grade represents 30% of the final grade of the project work. Please agree on a meeting time (in the period April 1-4) with the lecturer for reviewing and grading the first goal.
Second and final goal: poster is presented in the poster session.