Linear systems play a central role in countless problems including partial differential equations, inverse problems, and data analysis. Performing a matrix-vector multiplication, matrix inversion, or matrix factorization is computationally expensive if applied in its textbook form. This course will explore iterative and direct algorithms that accelerate these basic tasks. Examples of algorithms that may be covered include multigrid, fast summation methods, preconditioners, incomplete LU, interpolative decomposition, randomized algorithms, and low-rank factorizations.

To compile run:

make

Note compilation time might be longer than usual because the document contains the source code for all of the figures.

To remove all generated files run

make clean

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