CS 450: Numerical Analysis
Course Description
Numerical solution of mathematical problems arising in computational modeling and data analysis,
including proper problem formulation, selection of effective solution algorithms, and interpretation of
results. First half of course focuses on algebraic problems, including linear and nonlinear algebraic
equations, eigenvalue problems, and optimization. Second half of course focuses on analytic problems,
including numerical integration and differentiation and initial and boundary value problems for differential
equations. Prerequisites include linear algebra, multivariate calculus, and basic programming ability,
preferably using Python. No prior familiarity with numerical methods is assumed.
Course Prerequisite
•Linear algebra
•Multivariate calculus
•Basic programming ability using Python
Course Goals
By the end of the course, you will be able to:
•Learn how to formulate computational problems properly
•Learn how to select an effective solution algorithm for a given problem
•Learn how to interpret computational results properly
Textbook (Recommended but not required)
Michael T. Heath. Scientific Computing, Second edition, The McGraw-Hill Companies.
Sample syllabus - students will receive the detailed syllabus at the beginning of
the semester enrolled in the course.
