18.335J | Spring 2019 | Graduate
Introduction to Numerical Methods
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, …
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.
Learning Resource Types
assignment_turned_in Problem Sets with Solutions
grading Exams with Solutions
notes Lecture Notes
A six-petal symmetric graph.
A Newton fractal showing the basins of attraction for Newton iterations for 6th-roots of unity from different starting points in the complex plane. (Image by Prof. Steven G. Johnson.)