Course Description
This course offers an advanced introduction to numerical analysis, with a focus on accuracy and efficiency of numerical algorithms. Topics include sparse-matrix/iterative and dense-matrix algorithms in numerical linear algebra (for linear systems and eigenproblems), floating-point arithmetic, backwards error analysis, conditioning, and stability. Other computational topics (e.g., numerical integration or nonlinear optimization) are also surveyed.
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As Taught In: | Spring 2019 |
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Graduate
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Learning Resource Types
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Problem Sets with Solutions
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Exams with Solutions
notes
Lecture Notes