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
Problem Sets with Solutions
Exams with Solutions
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.)