- Schedule of Classes - February 24, 2020 7:14PM EST
- Course Catalog - February 24, 2020 7:15PM EST
Course information provided by the Courses of Study 2019-2020.
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.
When Offered Fall.
Prerequisites/Corequisites Prerequisite: CS 4220 or CS 6210.
Regular Academic Session.
Credits and Grading Basis
4 Credits Graded(Letter grades only)
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