Description

This is a collection of homeworks for "Calcolo Numerico" - 02023 course, Bachelor Degree in Computer Science, UniBo. These tasks are divided according to the topic in question seen during the course, in particular:

• LAB_1 contains a little introduction to the Python language and to some of the mathematical methods and functions that are used in the following LABs, for this reason I suggest taking a look.
• LAB_2 contains implementations and testing on direct (ex. Cholesky Fact.) and iterative methods (ex. Jacobi) to solve linear systems.
• LAB_3 contains approximations methods and image compression (SVD and LSQ problem) made with them.
• LAB_4 contains methods to solve non-linear equations.
• LAB_5 contains image blurring and deblurring exercises other than Lambda penalty, Gaussian Kernel, Gaussian Blur and image noise.

Dependencies

Mostly used numpy, matplotlib, math and time. If you don't have these libraries installed, I recommend you to do it. The workspace for me has been the IDE Spyder, from the Anaconda suite, which includes all the libraries mentioned above natively without need to download them.

Building, Running and Testing

If you don't like the IDE, you can test the programs with:

$ python3 file_name.py

And you can make sure that python is installed and check version with:

$ python3 -V

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cheers, hys 🎴