Data-Sparse Matrix Computations

Catalog description

Matrices and linear systems can be data-sparse in a wide variety of ways, and we can often leverage such underlying structure to perform matrix computations efficiently. This course will discuss several varieties of structured problems and associated algorithms. Example topics include randomized algorithms for numerical linear algebra, Krylov subspace methods, sparse recovery, and assorted matrix factorizations. Students must have a strong background in linear algebra, programming experience, and prior exposure to numerical methods.